Rapid screening, threshold, and diagnostic tests for evaluation of hearing.

ABSTRACT

A rapid screening, threshold, and diagnostic tests for evaluation of hearing includes techniques that are particularly suited for rapid objective hearing screening and evaluation of newborns or other patients who are unable or unwilling to provide reliable subjective responses. The hearing tests may be frequency specific or may evaluate overall hearing ability without special focus on frequency-specific loss. The tests involve the use of novel stimuli, signal processing, signal analysis, and statistical techniques, including the use of ramped stimuli and evaluating the changes that these stimuli evoke in the individual&#39;s brain activity at different moments in time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.10/913997 filed Aug. 6, 2004 and published as US20050018858, which is acontinuation-in-part of International Application No. PCT/US2004/003820,filed on Feb. 9, 2004, designating the U.S. and claiming the benefit ofU.S. provisional application Ser. No. 60/445,880, filed on Feb. 7, 2003,is a continuation-in part of International Application No.PCT/US03/03895, filed on Feb. 7, 2003, designating the U.S. and claimingthe benefit of U.S. provisional application Ser. No. 60/354,991, filedon Feb. 8, 2002. The content of said applications are incorporated byreference herein.

FIELD

This patent specification is in the field of auditory assessment. Itrelates, in part, to the rapid initial evaluation of hearing impairmentalso known as “universal newborn hearing screening”, but can be used forhearing screening and hearing evaluation of all ages. The hearing testsdescribed herein may be frequency specific or may evaluate overallhearing ability without special focus on frequency-specific loss. Itdescribes systems and methods for quickly and objectively performingscreening tests, threshold tests, and other types of evaluation of theauditory system using novel stimuli, signal processing, signal analysis,and statistical techniques. It further describes systems and methods forevaluating an individual's hearing abilities by using ramped stimuli andevaluating the changes that these stimuli evoke in the individual'sbrain activity at different moments in time.

BACKGROUND

Hearing-impairment affects about three newborn babies per thousand.These babies must be identified as early as possible so that adequatetreatment can be provided while the baby learns to hear and speak. Theearly detection and treatment of hearing loss helps the hearing-impairedchild to communicate effectively, and benefits society since thisindividual then requires less in the way of support. Governments inCanada, the USA and Europe have therefore instituted programs foruniversal newborn hearing screening (UNHS). Infants cannot reliablyrespond to sound—one cannot ask a baby if it can hear a sound. Screeningmust therefore be performed by non-behavioral/non-subjective measuressuch as by measuring the response of the ear or the brain to sound. Theear's response can be measured using “otoacoustic emissions” and thebrain's response can be tested using the “auditory brainstem response”(ABR). Both tests have their drawbacks. The otoacoustic emissions arefast, but do not check whether the brain is receiving information fromthe ear. The ABR tests take longer to record than would be optimal for ascreening test.

Currently, the click-evoked auditory brainstem response (c-ABR) is thestandard test for screening and evaluating infant hearing, but this isoften not automatic and is not frequency-specific. While this test willdetect gross hearing loss, it may not detect hearing loss at specificfrequencies that are important to the development of speech andlanguage, may require a long time in some cases, and may not have highaccuracy. Tone-evoked ABRs (t-ABR) can be used to assessfrequency-specific thresholds, but this testing procedure takes too longto be used routinely and the results can not be evaluated objectivelyand automatically by computer (Stapells, 1997). The use of otoacousticemissions (OAEs) is popular for quick screening of normal auditoryfunction, but will only detect peripheral loss and, while OAE methodscan detect hearing impairment, these techniques cannot be used todetermine the actual extent of the hearing loss. Frequency-specificaudiometric techniques that are rapid and accurate are thereforeimportant for detecting auditory thresholds and fitting hearing aids ininfants, or other patients, who are unable to easily provide reliableindication of their hearing abilities.

There are two main types of tests that may be implemented by anaudiologist: screening tests and threshold tests. These tests can becarried out for either frequency-specific stimuli, or for non-frequencyspecific stimuli. For example, current UNHS tests are done usingnon-frequency-specific click stimuli which contain energy at manyfrequencies. These tests indicate whether or not an infant has a minimalacceptable level of hearing and usually provide a simple pass/failresult. In the case where an individual fails a screening test (hearingthresholds are elevated), a threshold test can provide a furtherassessment of auditory abilities. In a threshold test, an individual'shearing is tested at successive intensity levels in order to determinethe thresholds of a patient (i.e., the minimum level at which a patientcan hear a sound). There is a need for objective frequency-specificthreshold tests that can be performed relatively quickly sinceconventional objective hearing threshold tests require on the order of30-40 minutes for obtaining a hearing threshold at multiple frequencies.This amount of time prevents the test from becoming clinically feasiblein some cases because, for example, a sleeping infant may wake up andstart crying, which makes testing impossible.

SUMMARY

The proposed system and methods for performing rapid hearing screeningand evaluation rely on novel stimuli, testing procedures, signalprocessing techniques, and statistical methods that allow the testing tooccur more rapidly, accurately, and thoroughly than currently availablemethods. The system may be used to test hearing in animals or humans ofall ages, including infants, elderly people, workers claimingcompensation for noise-induced hearing loss, and any other individualswho are unable or unwilling to provide reliable behavioral responsesduring conventional hearing tests. The system and methods may be used asa rapid screening test, providing a pass/fail result, and can also beused to provide threshold information or other information about anindividual's auditory system. The stimuli and methods can be used fortesting the aided and unaided hearing abilities of a patient.

The inventor has previously described novel techniques to evaluatefrequency-specific hearing thresholds by recording the brain's responseto frequency-specific sounds using auditory steady-state responses(ASSR), which are also known as steady-state auditory evoked potentials(SS-AEPs). These techniques provide a fast and accurate assessment ofhearing at specific frequencies (John et al, 1998; John et al 2000;PCT/CA 01/00715). The new set of techniques described herein canaccomplish rapid screening and hearing evaluation (both forfrequency-specific hearing abilities & gross hearing abilities) usingmethods that are faster and more accurate than those previouslydescribed both by the inventor and by others. The new set of techniquesdescribed herein can also evaluate hearing capacities of the auditorysystem, such as tuning curve characteristics, not measured by themethods described previously.

In one embodiment, a rapid and non-frequency specific hearing screeningtest is described which uses modulated noise stimuli and periodicstimuli (e.g. clicks) which are presented at a constant intensity and atsufficiently rapid rates so that SS-AEPs can be evoked within thepatient's EEG. In an alternative embodiment, a single modulationstimulus or band-pass noise stimuli are used to provide a rapid hearingscreening test which provides both non-frequency-specific andfrequency-specific information. In another alternative embodiment,threshold tests are described in which ramping stimuli andtime-frequency analysis techniques are used in order to determinenon-frequency specific hearing thresholds. In yet another alternativeembodiment, ramping stimuli and time-frequency analysis techniques areused in order to determine frequency specific hearing thresholds.Although these ramping stimuli tests may be used to rapidly providehearing assessment for at least one stimulus at many intensities,thereby providing threshold information, this information can also beused for screening purposes. In another alternative embodiment, rampingstimuli are used to reliably determine the physiological modulationtransfer function of an individual. This information can be used, forexample, to determine optimal modulation rates are for an individual.Optimal modulation rates are rates that evoke responses with goodsignal-to-noise levels. These rates can be used in order to increase theefficiency, sensitivity, and specificity of hearing evaluation usingsubsequently performed audiometric tests. In another alternativeembodiment, ramping stimuli are used to determine the fine structure ofthe audiogram and tuning characteristics of an individual's auditorysystem. In the embodiments which rely on evoked potentials which changeover time due to a ramping stimulus (“R-AEPs”) and time-frequencyanalysis, methods such as “threshold series” and “homogeneity criteria”may be used to improve the accuracy and clinical values of the tests. Inthe embodiments which rely on SS-AEPs and frequency analysis, usingmethods such as “significance series” and “homogeneity criteria” may beused to improve the accuracy and clinical value of the tests. These newtechniques can be used to test one ear at a time, or can be used to testboth ears simultaneously. In addition, several stimuli cansimultaneously be presented within each single ear.

The inventor has published a series of scientific publications on usingmultiple modulated tones to efficiently obtain frequency-specifichearing thresholds. These methods are known as the Multiple AuditorySteady-State Response (MASTER) technique (John et al., 2000). Thetesting methods described herein are novel from and offer advantagesover those used by the MASTER technique. Methods are described toperform screening tests which use SS-AEPs evoked by stimuli that arerelatively non-frequency specific. Because responses to non-frequencyspecific stimuli, such as clicks and amplitude modulated noise (e.g.,broadband or band-pass noise) are larger than responses tofrequency-specific stimuli, the novel methods described here can be usedin tests which are faster and more reliable, and which can occur atlower intensities than permitted by the prior art techniques. Thesequalities are advantageous for rapid screening tests.

In one embodiment a screening test is described which uses modulatednoise stimuli. SS-AEPs evoked by amplitude modulated noise have beeninvestigated (e.g., Rees et al., 1986). The Inventor (John et al, 1998),used amplitude modulated noise and showed that the evoked steady-stateresponses were larger than those evoked by amplitude modulated tonalstimuli presented at fairly high intensity levels. However, untilseveral experiments were done by the Inventor using lower intensitiesand rapid testing times, as are reported here, it was not understoodthat i) the increased size of these responses, would also be robust atlow intensities, ii) the size of these responses would be betweenapproximately 200-400% the size of the responses found with tonalstimuli, iii) the size of these responses would be sufficiently largerthan the background EEG-noise levels, even at low intensities, to beeasily detectible, and iv) unlike frequency-specific tonal stimuli, theresponses would be reliably evoked in a short amount of time in allindividuals with normal hearing, and thereby permit rapid and reliabledetection of the responses. It is these qualities that enable thesestimuli to be used in a rapid and objective screening test. SS-AEPs arenormally recorded to modulated tonal carriers that sometimes requirequite of bit of time to become significant, and are therefore notappropriate for a rapid screening test.

Additionally, click stimuli can also be used in an SS-AEP basedscreening test using the new methods. Click stimuli have been used forscreening for many years, but were not presented at sufficiently rapidrates to generate SS-AEPs. Importantly, unlike conventional c-ABRscreening tests, when clicks are used to generate SS-AEPs in accordancewith the present disclosure, the clicks must occur at a repetition ratethat causes the time between click stimuli to be an integer sub-multipleof the epoch length. Additionally, creating various types of transientstimuli which are characterized by this integer sub-multiple repetitionrate is another aspect of the methods described herein.

In accordance with this disclosure a set of methods is described forusing ramping stimuli to generate R-AEPs which are analyzed in thetime-frequency domain (“ramping techniques”) to accomplish differenttypes of objective audiometric tests. Ramping stimuli (also referred toas “ramp stimuli”, “ramped stimuli”, or “R-AEP stimuli), are stimuli forwhich a particular characteristic is changed or “ramped” over time in anintentional manner. R-AEPs can be evoked by either frequency specific ornon-frequency specific stimuli. In one embodiment, a ramping techniqueutilizes an intensity ramp and provides information about responsemagnitude at several intensity levels. Similar to the use of a rampingtechnique or “a sweep technique” in the visual modality which was usedto test visual acuity (Norcia and Tyler, 1985), ramping techniques havebeen attempted for auditory stimuli. Linen et al presented rampingstimuli at intensities which ramped from −21 to 60 SL (Linden et al,1985), but concluded that the variability in the estimates of thresholdwas too large for ramping stimuli to be incorporated into an accurateclinical method. However, the present disclosure provides signalanalysis and signal processing methods which are novel and offeradvantages over those of the prior art by using methods which decreasethe variability of both the noise and signal estimates derived from thea patient's EEG data, and by providing methods for estimating thereliability of threshold estimates derived during the testingprocedures. For example, methods are described for rejecting data andfor substituting this rejected data with acceptable data. Further,methods are described for increasing the utility of the data bydynamically changing the ramping stimuli during the testing procedureaccording to the characteristics of the R-AEPs. Methods are alsodescribed for testing the reliability of threshold estimates.Accordingly, the ramping techniques of the present disclosure describeobjective audiometric tests that rely upon novel methods concerningtesting protocols, stimulus presentation, data collection, signalprocessing, and data analysis (e.g. determination of thresholds) whichlead to more accurate and more efficient testing methods.

An advantage of the R-AEP tests are that they may provide a moreaccurate assessment of threshold than SS-AEP tests which iterativelytest at different intensities. For example if a subject is able toproduce an evoked response to a stimulus presented at 48 dB, but thestimulus is tested in 10 dB steps, e.g., at 50 dB and then at 40 dB,then the threshold estimate will be off by 8 dB. Decreasing the stepsize may give more accurate estimates of threshold, but will alsolengthen test times.

A system and methods are provided that utilize the aforementionedtechniques to measure hearing quickly and automatically. The methods canbe completely objective, which means that subjective responses are notrequired from the person being tested and subjective interpretations arenot required from the clinical personnel conducting the test. The systempresents acoustic stimuli to a patient, and concurrently records brainactivity (“EEG”) with the premise that if the brain activity is alteredby the stimuli, then the individual can hear the stimuli being tested.The system and methods may be realized through both hardware andsoftware components, although different embodiments may rely more uponhardware or software. The different methods described herein may berealized as software modules of the system.

In particular, the present disclosure provides a system and a set ofmethods for performing objective audiometry by, for example, choosingtest protocols, presenting stimuli while simultaneously acquiring EEGdata, increasing data quality by modifying and organizing the EEG datawithin the data set, performing signal processing and analyzing the EEGdata in the frequency domain or time-frequency domain to generate resultdata which can include an amplitude spectrum, a spectrogram, estimatesof evoked responses such as SS-AEPs and R-AEPs and estimates of thebackground EEG-noise levels, and using the result data to provide TestResults. The system further enables displaying the results of ongoingtesting and the final Test Results as well as the storage of the rawdata and Test Results for subsequent viewing and/or analysis. Thesoftware is also adapted to carry out tests of data quality andconsistency in order to determine if the results are meaningful andreliable.

Particular types of stimuli are disclosed which increase the amplitudeof the resulting responses or offer other advantages over alternativestimuli when used in a screening (or threshold) test. These stimuliinclude amplitude modulated broadband noise, and band-pass noise(including narrow-band noise) which can be either modulated orun-modulated. These stimuli also include ramped acoustic stimuli thatare characterized by continuously changing characteristics, for example,time-varying intensity. These stimuli also include multiple intensitystimuli in which at least 2 intensities are simultaneously tested. Thesestimuli may be formed by using either different or the samemodulation/repetition rate for each carrier signal of the stimulus.

The systems and methods further comprise a database which containsnormative data which may be population normative data or self norm data(prior data collected from a subject).

Further objects and advantages will be apparent from the followingdescription, taken together with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the written description and to show moreclearly how it may be carried into effect, reference will now be made,by way of example, to the accompanying drawings which show preferredembodiments and in which:

FIG. 1 a is a block diagram of a preferred embodiment of a system;

FIG. 1 b is a flow diagram illustrating a general objective auditorytest methodology;

FIG. 2 shows amplitude spectra after 8 and 16 seconds of recording inwhich two components in the spectra that are at the frequency ofmodulation of the periodic acoustic stimulus are shown;

FIG. 3 shows pilot study data showing the amount of time required forresponses to reach significance in a test group of 20 ears using anexponentially modulated noise carrier stimulus. The y-axis spans from 0to 20 and signifies the total number of ears for which the evokedresponse did not reach significance. By 160 seconds evoked responses hadreached significance for all ears;

FIG. 4 a shows a significance series for responses that was above thesubject's hearing threshold, the x-axis is epoch number and the y-axisis probability level;

FIG. 4 b shows a significance series for responses that was below thesubject's hearing threshold, the x-axis is epoch number and the y-axisis probability level;

FIG. 5 is a series of plots that illustrate a procedure for generating aspectrogram and amplitude and phase plots using data from 1 subject;

FIG. 6 shows an iterative testing procedure having subroutine A andsubroutine B in which subroutine A includes performing a ramping testand estimating a hearing threshold, and subroutine B includes generatinga threshold series wherein s subroutines A and B are iterativelyrepeated until statistical analysis performed in subroutine B indicatesthat the results meet a criteria and the testing of that stimulus can bestopped;

FIG. 7 shows the instantaneous intensities of ramp stimuli that are usedfor an embodiment of a method for iterative testing and an embodiment ofa method for accomplishing a Dynamic Iterative Ramp Test;

FIG. 8 shows an example of evoked response data, noise estimates, andthe range of instantaneous intensity of stimuli used in an embodiment ofthe Dynamic Iterative Ramp Test, which is based upon response data;

FIG. 9 shows-an example of how stimuli may be selected in a DynamicIterative Ramping Test, based upon response data, and shows an exampleof the Dynamic Linked Ramping Test technique;

FIG. 10 shows an example of how stimuli may be selected in a DynamicIterative Ramping Test, based upon response data, when multiplefrequency specific stimuli are tested at the same time; and

FIG. 11 shows examples of multiple intensity amplitude modulated noisestimuli that were created using sinusoidal or exponential envelopes (andwell chosen stimulus parameters), or which were created using clicks.

DETAILED DESCRIPTION

A system and methods are disclosed, in accordance with the presentinvention, for using SS-AEPs and R-AEPs to achieve objective audiometryby relying upon novel stimuli, test procedures, data criteria, signalprocessing, statistical, and data analysis techniques. A set of methodsis disclosed for using the system to rapidly screen for hearingpathology, to obtain estimates of hearing threshold, or to evaluateother characteristics of the auditory system. The basic hardware andsoftware components of the system will be discussed first. Methods forhearing evaluation, such as for screening and for threshold estimationwill then be discussed. These techniques utilize novel types of acousticstimuli, data analysis, noise reduction and response detection methods.Methods for objective audiometric testing based on R-AEP stimuli will bediscussed with respect to obtaining auditory thresholds and other typesof information about the auditory system. The system and methodsdescribed herein enable objective auditory testing to be achievedquickly and accurately. This allows the test to be performed when aninfant is sleeping and allows many infants to be tested in a short time.Several fundamental methods of achieving a faster test are by making thesignal larger, making the noise background smaller and less variable,modifying the test dynamically based upon response data andinterpolating from existing data. New methods which address each ofthese areas are employed.

Hardware and Software Components

Referring now to FIG. 1 a, shown therein is a system 10 for performingobjective audiometry that includes a processor 12, a data acquisitionsystem 14 that can be a board having a digital to analog converter (DAC)16 and an analog to digital converter (ADC) 18, an audiometer 20 havingstimulus filtering circuitry 22 and stimulus amplifier circuitry 24, atransducer 26, sensors 28, response amplifier circuitry 30, responsefilter circuitry 32, a storage device 34 and a visual display 36. Theprocessor 12 contains an auditory evaluation program 40, which isrealized using hardware or by a software program, comprising a signalcreator module 42, and an analysis module 46 having a noise reductionmodule 48 a detection module 50, and a database 52 having a plurality ofnormative values.

A personal computer (PC), for example a Pentium 750 running Windows2000, may provide the processor 12, storage device 34 and displaymonitor 36. The auditory evaluation program 40 is run on the PC and thedatabase 52 can be stored in the memory and storage device of the PC andcan communicate with the auditory evaluation program 40. Alternatively,these components may be effected on a laptop, a programmable handheldcomputing device, such as a palmtop, or a dedicated electronics device.

The objective audiometric test system 10 can be used to assess theauditory system of a patient 60 by presenting acoustic stimuli to thepatient 60, while simultaneously amplifying and digitizing sensedpotentials into EEG data (This can also be referred to in thisapplication as “data”, “response data”, or “evoked response data”). TheEEG data is then processed and statistically evaluated to determine ifthis data contains evoked responses (these can be termed, “signals”,“responses”, “ASSRs”, “auditory steady-state responses”, “SS-AEPs”,“ramping evoked responses” “RAMPERs” or “R-AEPs”). For example, signalprocessing may show the responses that are statistically significantlydifferent than the background EEG-noise levels. The design of theobjective audiometric test system 10 follows clear principles forcarrying out test protocols, generating the acoustic stimuli, acquiringresponse data, performing signal processing on response data to ensurethat data which does not meet specific criteria are rejected therebyproviding the frequency-domain or time-frequency domain signalprocessing routines with high quality accepted data epochs in order togenerate “result data” (e.g. a spectrogram, amplitude plot, phase plot,statistical characteristics of signals and noise levels such as mean andstandard deviation, threshold series data, significance series data),statistically and objectively evaluating the result data to obtain“summary data” (e.g., statistical probability of the presence of aresponse, results of the application of statistical conditionalcriteria, estimates of the auditory function of an individual such asfrequency-specific intensity thresholds, results of regressionoperations) and using the summary data to produce “test results”, whichcan include the summary data, but which also include results such asPass/Fail status.

The data acquisition system 14 can be a commercial data acquisitionboard (e.g., AT-MIO-16E-4) available from National Instruments, or acomparable alternative. The data acquisition system 14 allows for theoutput of data (e.g., the stimuli) via the DAC 16 as well as the inputof data via the ADC 18.

The output from the DAC 16 is sent to a signal conditioner such as anaudio amplifier or audiometer 20 which is under the control of theprocessor 12. The audiometer 20 acts to condition the stimulus that ispresented to the patient 60 via the stimulus filter circuitry 22 and thestimulus amplifier circuitry 24. Rather than using the audiometer 20,functionally similar amplifying/attenuating and filtering hardwareand/or software can be incorporated into the objective audiometric testsystem 10 to control the intensity and frequency content of the stimulusand the operational settings (e.g., band-pass, filter characteristics,gain) for both the filtering and amplifying circuitry. The stimulus ispresented to the patient 60 via the transducer 26 which may be at leastone free-field speaker, headphone, insert earphone (e.g. ER3A fromEtymotics Research) or bone conduction vibrator. The transducer 26allows the steady-state or ramping stimulus to be presented to the leftand/or right ears of the patient 60.

While the stimulus is presented to the patient 60, the EEG issubstantially simultaneously sensed using sensors 28 which are typicallyelectrodes. For example, one active electrode can be placed at thevertex location of the head of the patient 60, one reference electrodecan be placed on the neck of the patient 60 and a ground electrode canbe placed at the clavicle of the patient 60. Other configurations forthe electrodes are possible as is commonly known to those skilled in theart. It is also possible to use more electrodes than are shown in FIG. 1a.

The sensed EEG data is routed to the response amplifier circuitry 30,which is typically a differential amplifier, for amplifying the sensedEEG data to a level that is appropriate for the input range of the ADC18. The response amplifier circuitry 30 may use a gain of 50,000.Alternatively, the response amplifier circuitry 30 may be a programmableamplifier that provides a variable gain that may be under the control ofthe auditory evaluation program 40. The amplified sensed EEG data isthen sent to the response filter circuitry 32 which filters theamplified sensed EEG data such that sampling can be done withoutaliasing by the ADC 18. The response filter circuitry 32 may have aband-pass of 1-300 Hz. However, the band-pass range can change dependingupon various considerations such as the characteristics of the stimuli(e.g. modulation rates) that are being used for testing. The ADC 18digitizes the filtered amplified EEG data at a rate of approximately1,000 to 5,000 Hz, or another suitable rate, provided that the upperlimit of the band-pass range of the response filter circuitry 32 is setso that the Nyquist rate is not violated as is well understood by thoseskilled in the art.

The objective audiometric test system 10 can be embodied in variousways. For example, multiple outputs may also be used (e.g. an eightchannel DAC) to create the acoustic stimuli that are presented to eachear of the patient 60. This would allow some components of the stimulusto be easily dynamically manipulated in real-time independently from theothers. The auditory evaluation program 40, may realize its noveltesting methods and test protocols using a software program, createdwith any modern programming language such as LabVIEW, MATLAB, or C or),or in a combination of these languages, since these languages allowcalls to external routines (for example, LabVIEW can call a MATLABroutine). The software platform can be similar to the MASTER (MultipleAuditory Steady-State Response) system (John & Picton, 2000;www.blsc.com), which utilizes a graphical user-friendly interface basedupon a series of interactive screens which allow users control theauditory evaluation program 40, for example, to create and load testprotocols and their parameters, to view and modify stimuli, and to view,on the visual display 36, the incoming evoked response data in real timein both the time and frequency (or time-frequency) domain, and to viewsummary results and test results for the patient 60. The auditoryevaluation program 40 can also save the test results and recorded EEGdata on the storage device 34, and also allows the test results to beprinted by a printer (not shown). The auditory evaluation program 40enables the user to choose different methods of viewing, storing,combining and analyzing data. The data can include either the raw dataor averaged data, and can include the responses from a single patient orfrom a plurality of patients. Portions of the data may also besubtracted from other portions, in order to enable the user tocalculate, for example, derived-band responses.

The auditory evaluation program 40 controls test signal generation (“thestimuli) via the signal creator module 42. The auditory evaluationprogram 40 also allows the user to select from a variety of pre-definedobjective audiometric tests “test protocols”. The settings or“parameters” of the test protocols may be modified by the user, and inorder to define, for example, the stimuli (e.g., modulation rate,carrier frequencies, intensities, characteristics, such as range, of aramping stimulus), maximum duration of the test, criteria to be usedduring the test (e.g., homogeneity criteria), signal analysis methods tobe used by the test (e.g., weighted averaging, method ofrejecting/substituting data epochs), and other features of the test. Thesignal creator module 42 allows the creation of the time serieswaveforms that are used as the acoustic stimuli. The auditory evaluationprogram 40 also controls analog-to-digital conversion anddigital-to-analog conversion according to the protocol of the auditorytest that is being performed. The auditory evaluation program 40comprises a plurality of modules that are not all shown in FIG. 1 a toprevent cluttering the Figure. For example, there are modules forreviewing raw data that is read from disk and for viewing test resultsfor each frequency tested and at several intensities, for example, in anaudiogram. Each of the different test protocols which rely on themethods described herein can be realized in a different software module.The auditory evaluation program 40 enables the test to be accomplishedby performing the data analysis/management, signal processing,statistical processing, in order to generate summary results and testresults

During an auditory test, the auditory evaluation program 40 analyzes thesensed evoked response data via the analysis module 46 that includes thenoise reduction module 48 and the response detection module 50. Thenoise reduction module 48 may employ weighted averaging, time averagingand/or various types of artifact rejection (which will all be describedlater in more detail) on the evoked response data. The evoked responsedata is then analyzed by the detection module 50 to determine whether atleast one evoked response is present within the sensed evoked responsedata. The detection module 50 may employ a phase weighted t-test, orother methods which will later be described in more detail. In the caseof R-AEPs, the detection module 50 may also be used to determine if areliable threshold estimate has been detected.

The auditory evaluation program 40 also communicates with the database52 which contains a plurality of normative data values, which weregenerated from normal populations, which relate to a variety ofparameters for audiometric testing using the different test protocols.For instance, the database 52 includes normative mean and variancevalues for measures such as phase data which can be used during aphase-biasing t-test. The database 52 can also include normative values,according to test protocols (e.g., relating to different intensities ofthe stimuli and different modulation/repetition rates), for suchmeasures as the amplitudes and phases of evoked responses, responseamplitude vs. intensity curves (slopes of R-AEPs over time, or SS-AEPsat different intensities) and other measures, which can be used to helpdetect and evaluate SS-AEPs or R-AEPs and to determine whether these areindicative of normal or abnormal hearing. The database 52 can alsoinclude normative data which is a self norm data. Self norm data isderived from the individual being tested, and may be obtained, forexample, from prior data obtained for that patient to stimuli which mayhave be presented at a different intensity, or from data recordedearlier in the current test (e.g., to the current stimuli), or fromresponses to other stimuli in the test being conducted. The normativedata which are organized in terms of the patient's characteristics suchas age, sex. The database 52 can also contain normative values forbackground EEG-noise levels, and these values can be defined differentlyfor different test durations. The database 52 can also contain normativetesting durations which are required for the responses evoked by stimuliused in different test protocols to become significant for a proportion,e.g., 80%, of the population.

The auditory evaluation program 40 interacts with the signal creatormodule 42 to permit the user to select a particular type of acousticstimulus and control the parameters of the selected acoustic stimulus.In addition to the amplitude and frequency modulated stimuli that aredescribed in PCT patent application No. PCT/CA 01/00715, and theparameters that are available for modification (e.g., modulation rate,carrier frequency), the signal creator module 42 allows for the creationof un-modulated or modulated noise which may be band-limited, and allowsfor the creation of periodically repeating transient stimuli and thecreation of ramp stimuli by allowing ramp functions to be defined.Ramped functions are defined by parameters such as amplitude range ofthe ramping function and duration of the ramping function (which bothdefine the rate of change over time), as well as the ramping functionshape (e.g., linear, logarithmic, smooth or stepped, symmetrical andnon-symmetrical). Additionally, the upward or downward portion of aramping function may consist of more than one slope. For example, whenthe intensity of the stimulus is the characteristic being ramped, and adual slope function is used, the slope of the ramp may be smaller whilethe intensity of the stimulus is lower and may change to a steeper slopewhen the ramping function traverses the higher intensity range. If dualslope functions are used, then two threshold estimations may be computedseparately, using the R-AEP responses evoked by the two separateportions of the ramp.

Once the stimulus parameters are chosen, the signal creator module 42automatically adjusts them in order to ensure that certain rules arefollowed. For instance, the signal creator module 42 ensures that aninteger number of cycles of the modulation signal fit in the outputbuffer of the DAC 16 and the input buffer of the ADC 18. In the case oftransient stimuli, the repetition rate is chosen so that the duration ofthe stimulus combined with the post-stimulus interval that occurs priorto the next stimulus presentation results in a duration which is aninteger sub-multiple of the duration of an input/output buffer (i.e., ofthe epoch length). This is important to avoid spectral spreading in thegenerated acoustic stimulus as well as to avoid spectral spreading inthe sensed EEG data which are digitized by the ADC 18. The signalcreator module 42 may also be used to present test signals to thepatient 60 with constant peak-to-peak amplitudes or constant RMSamplitudes, whereby the amplitude of the envelope of the test signal isincreased to compensate for the modulation depth of the stimuli (e.g.,80% amplitude modulation). Any stimulus (e.g., steady-state noise,amplitude modulated tones, and transient stimuli) which is able toproduce an SS-AEP can be referred to as an “SS-AEP stimulus”. Anystimulus for which a given parameter (e.g., intensity, modulation rate,carrier frequency) is ramped over time in order to produce an R-AEP, canbe referred to as an “R-AEP stimulus”. The signal creator module 42 isable to create many types of SS-AEP stimuli and R-AEP stimuli, as wellas other types of stimuli (e.g. un-modulated masking stimuli), forexample, according to user specifications, dynamically, according to theprocedures of the testing methods as will be described, according tospecifications of different test protocols which may be loaded fromdisk, as well as according to specifications which are defined withinthe auditory evaluation program 40 for the various tests described.

The signal creator module 42 can create a variety of test signals thatcan be used to evoke the SS-AEPs and R-AEPs. These test signals caninclude amplitude modulated noise (including broad-band, band-pass, &narrow-band noise), transient stimuli, single-modulation frequencystimuli, and ramp stimuli. The signal creator module 42 can alsogenerate stimuli such as high-pass, low-pass, or band-pass noise, all ofwhich can be either modulated or un-modulated, which can function astest signals or as “masking” stimuli. In the case of band-pass noise,the signal creator module 42 may allow the user to adjust the band-passand band-stop characteristics, including the roll-off. The signalcreator module 42 can also generate a train of rarefaction,condensation, or alternating polarity clicks, which repeat at aninterval that is a sub-multiple of the epoch length.

The auditory evaluation program 40 permits the user to define testprotocol parameters such as the sampling rate of the ADC 18, thesampling rate of the DAC 16 (which must be a multiple of the A/D rate)and the epoch duration (i.e. the size of the input buffer contained inthe ADC 18). The user may also define an artifact rejection techniqueand associated parameters, calibration coefficients which adjust theamplitude and phase of the estimated responses based upon recordingparameters such as the filter settings, and choose various signalprocessing options related to the processing of the evoked potentialdata The artifact rejection level may be based one or more criteria. Forexample, artifact rejection criteria may include an absolute thresholdvalue, the average amplitude of a high-frequency range of the evokedpotential data, or values based upon certain characteristics such asstandard deviation of the power in a frequency range of the evokedresponse data collected for that patient earlier in the recording.

Prior to running a test, the auditory evaluation program 40 permits theuser to view the stimuli that will be presented to the patient 60.During a test, the user may view the sensed EEG data for the currentepoch that is being sampled. The user can also view the amplitudespectra, or spectrogram (an amplitude and phase plots) computed upon theaverage sweep (a sweep is a concatenation of epochs and the averagesweep is the result from averaging a plurality of sweeps). When thespectra of the average sweep are displayed, the frequencies of theSS-AEPs or R-AEPs in the EEG data nay be highlighted for easy comparisonwith background EEG-noise activity (i.e. background noise). The auditoryevaluation program 40 also allows the user to view both the numericaland graphical results of statistical analyses that are conducted on theevoked response data to detect the presence of at least one evokedresponse to a stimulus.

When performing a test which includes single or multiple stimuli, orwhich includes stimuli whose parameters are sequentially changed, forexample, which are presented at two or more different intensity levels,then the resulting data must be meaningfully organized. In oneembodiment, the auditory evaluation program 40 can use the analysismodule 46 to organize the evoked response data into a data set so thatthis data can be analyzed intelligently. The data set my contain datawhich it organizes into units such as sweeps, accepted data epochs,rejected data epochs, and averaged sweeps, summary results, statisticalresults, which can be organized based upon the stimuli and intensitiestested. The data set may contain many data structures, such as matricesand vectors which may be multidimensional as will be described. The dataset enables the auditory evaluation program 40 to efficiently performsignal processing on data needed to obtain the test results.

In one embodiment, the data set is organized into epochs which have beenaccepted or rejected. The analysis module 46 keeps track of the stimulusparameters which were used to collect each-epoch, and uses thisinformation to form sweeps. The sweeps can be averaged together to formaverage sweeps, based upon stimulus parameters. For example, if sweeps1-10 were collected when stimuli were presented at 50 dB SPL, and sweeps10-25 were collected when stimuli were presented at 40 dB SPL, then twoaveraged sweeps can be created from sweeps 1-10, and sweeps 10-25,respectively. The sweeps can also be conceptualized, organized, andindexed according to epochs. For example, if 16 epochs are included ineach sweep, then the data in sweeps 1-10 can organized and indexed bythe analysis module 46 as a data set which includes 16 columns of dataand 10 rows of data. The analysis module 46 dynamically organizes thedata matrix into sweeps, based upon the status of epochs as accepted orrejected, which may change as a test progresses. When multiple stimuliare used, separate sweeps and separate average sweeps may be created foreach stimuli to be evaluated, even when these multiple stimuli arepresented at a single intensity. Further, the analyses module 46 mayre-organize the evoked response data into numerous additionalcomputational matrices in order to efficiently and rapidly create thesweeps and averaged sweeps needed to analyze the evoked response dataand produce summary results for a test. Programming languages such asMATLAB provide data “structures” within which manipulation, storage, andorganization of matrix data is handled easily. For example, “cellarrays” enable a matrix to be created, where each cell is a vector ormatrix. Accordingly, a 2-by-2 cell array “A” can be created where,A(1,1)={['all accepted epochs']}; A(1,2)={'all rejected epochs'};A(2,1)={'individual sweeps for 50 dB SPL}; A(2,2)={' individual sweepsfor 50 dB SPL }. In the cell 1,1 of the cell array, the ‘all acceptedepochs’ data is a matrix where each row is an epoch, and each column thevalues of the evoked response data at different moments in time. Becausecell arrays may be “nested”, cell A(2,2) can be a matrix which containsthe individual sweeps for 50 dB where all evoked responses are evaluatedwith the same data (or the same data can be indexed in an n by 16 cellarray, where n=number sweeps, and 16 epochs per sweep are stored in 16columns of the cell array), or can be a (1,4) cell where each cellcorresponds to 50 dB SPL sweep data for each of 4 evoked responses beingevaluated. Matlab could also manage and index the data usingmulti-dimensional arrays, matrices, & pages. Further, just as theanalysis module 46 can analyze and organize the data matrix so that itanalyzes data obtained at different intensity levels separately, it canalso analyze the data (and any measures derived from these matrices)which was collected when different stimulus parameters were in effect,to estimate thresholds. The analysis module 46 can analyze the summaryresults obtained for all the different stimuli and stimulus parametersused during a test to produce test results, using, various techniquessuch as regression.

The auditory evaluation program 40 has options for collecting anddisplaying data appropriate for the test protocol. For example, as iscommonly incorporated into clinical audiometric devices, the parametersfor several clinical protocols can be stored in several parameter filesto enable several tests to be run automatically, for example, each withseveral stimulus intensities or different stimuli. The test resultsobtained from using different stimuli and different stimulus intensitylevels can be displayed in several Test Summary screens where the testresults of the patient 60 are presented, for example, in traditionalaudiogram format.

FIG. 1 b illustrates the general steps undertaken by the objectiveaudiometric test system 10. The objective audiometric test system 10first generates a test signal in step M1 which is appropriate fortesting an aspect of the auditory system of the patient 60. The testsignal may comprise a wide variety of signals including amplitudemodulated noise, click train stimuli, ramped signals, and the like. Thenext step M2 is to transduce the test signal to create an acousticstimulus and present this stimulus while simultaneously recording theEEG data at step M3. The presentation of the stimulus and theacquisition of the EEG data should be synchronized to accuratelyrepresent signals of interest. The next step M4 comprises signalprocessing and analyzing the recorded EEG data to determine whetherthere are any responses present in the EEG data or if certain criteriahave been met so that further data acquisition is not necessary and theauditory test, or some portion of the auditory test, may halt. This stepmay also include estimating an auditory threshold based upon responsesin the EEG data from the patient 60. Step M4 will typically involveperforming a noise reduction method on the EEG data to produce datawhich has less noise and which is more homogenous in its characteristics(termed “noise reduced data”) and then applying a detection method tothe noise reduced data. In the next step M5, Test Results are reported.The steps outlined in FIG. 1 b may be part of a larger audiometric testbattery that will involve iteratively performing each of the stepsseveral times and at different intensities or with different stimulusparameters. These particular audiometric tests and the steps which areinvolved are discussed in more detail below.

Summary results include from the process of FIG. 1 b, for all stimulitested, and for each intensity level or ramping function tested,information concerning the amplitude, phase, statistical probability ofthe presence of an evoked response (likelihood a sound was heard),confidence limits, noise level estimates, and other measures that may bederived from the data. The Summary Results can be included within theTest Results, however, the test results also include the overall resultsof the test. For example, the summary results may indicate that theprobability of the presence of a response was 0.01 at 50 dB, and 0.29 at40 dB. The test results will use this to, for example, provide aPASS/FAIL result, or to state threshold for the stimulus is 50 dB.Further, by means of regression, or by subtracting a constant, the TestResults may indicate that the threshold for the stimulus is 40 dB.Accordingly, the Test Results include information which provides theclinician with meaningful audiometric results.

SS-AEP/R-AEP Detection

The EEG data that is sensed during the presentation of multiple SS-AEPand R-AEP stimuli may contain several superimposed responses (as in thecase of binaural or multiple-stimulus testing) as well as other EEGactivity which is regarded as background EEG-noise (which is estimatedin the background EEG-noise level). Due both to the small size of theresponses and the superimposition of several responses when usingmultiple-stimuli, it is difficult to distinguish the SS-AEP/R-AEPresponses in the time domain. However, if the EEG data is converted intothe frequency domain, using, for example, a Fast Fourier Transform(“FFT”), and the amplitude and phase of each evoked response can bemeasured reliably at the specific frequency of eachmodulation/repetition rate in the stimulus.

The SNRs of the SS-AEPs and R-AEPs are very small compared to thebackground EEG. Accordingly, a sufficient amount of EEG data must becollected in order to increase the size of the response datasufficiently to be recognized as either stable (e.g. phase coherencestatistics), or statistically different from background EEG-noise levels(e.g., F-ratio). Conventional approaches to increase the SNR of theevoked response data include artifact rejection and time averaging.Although there are advanced signal processing techniques which offeradvantages over simple artifact rejection based upon a fixed voltagelevel of the EEG signal (some of which are described below), this typeof conventional approach can be implemented by the noise reductionmodule 48, in part because these techniques are still fairly popularwith clinicians and research scientists in the field of audiology.Artifacts may introduce large noise spikes that are due to non-cerebralpotentials such as movement of facial muscles or the like, and thisenergy can decrease the detection of responses when not rejected.

Artifact rejection may occur for any epoch of EEG data that is acquiredduring SS-AEP and R-AEP testing. In SS-AEP recording, if an epoch isrejected, the next epoch that does not exceed the artifact rejectioncriteria is concatenated to the last acceptable epoch. Thisconcatenation procedure does not produce discontinuities in the datawhich are of any concern because each stimulus which evokes the SS-AEPsis constructed so that each epoch contains an integer number of periodsof the evoked steady-state responses (John and Picton, 2000). The noisereduction module 48 can also perform other types of noise reductiontechniques such as weighted averaging, data substitution, datamanagement tasks, etc. The last two techniques may be required to ensurethat the recorded EEG data epochs which are accepted comply withhomogeneity criteria (see section entitled Data Quality ControlTechniques). The objective audiometric test system 10 contains the noisereduction module 48 which may be adapted to employ artifact rejection inwhich epochs are rejected based on specific criteria such as an amountof high frequency (e.g., 70-200 Hz) activity. The noise reduction module48 may further employ other types of weighted averaging as previouslydescribed by the inventor (John et al., 2001, PCT/CA 01/00715). As willbe described later, artifact rejection and noise weighting are morecomplicated for R-AEPs because a parameter of the stimulus, such as theintensity of the stimulus, is constantly changing. Accordingly, if anepoch is rejected, the subsequent epoch can not take its place becausethis epoch contains R-AEPs elicited by acoustic stimuli with a differentrange of intensities.

As has been described by the Inventor (John and Picton, 2000), timeaveraging comprises concatenating epochs to form sweeps. A plurality ofsweeps are then averaged in time to yield an-average sweep. Timeaveraging reduces the level of background EEG-noise activity that is nottime-locked to the stimuli. As each average sweep is obtained, it isconverted into the frequency domain, or time-frequency domain, by thesignal analysis module 46, for example, via an FFT, short time FourierTransform (STFT), wavelet analysis, filtering, adaptive filtering,independent component analysis, or other time-domain tofrequency-domain, or time-domain to time-frequency domain, conversionprocedure. When converting data into the frequency domain using methodssuch as the FFT, the sweep duration is an issue since increasing thesweep duration distributes the background noise power across more FFTbins. Thus, increasing the duration of the sweep increases thefrequency-resolution of the FFT. The specific frequencies available fromthe FFT are integer multiples of the resolution of the FFT which is1/(N_(t)), where N is the number of data points and t is the samplingrate. One possible implementation uses a sampling rate of 1000 Hz, anepoch length of 1024 points and sweeps that are 16 epochs long (16,384points). Accordingly, the resulting frequency resolution is 0.61 Hz(1/(16*1.024*0.001)) and the frequency region in the FFT spans DC (0 Hz)to 500 Hz. Alternatively, sweeps may also be other durations, such as 1,8 or 12 epochs.

The detection module 50 may provide an EEG-noise level estimate whichcan be derived from data at neighboring frequencies in the amplitudespectrum. Neighboring frequencies are close in frequency to a responsefrequency but no SS-AEP occurs in the neighboring frequencies. If therewere no SS-AEP in the recorded data then the energy in the FFT whichoccurred at the modulation frequency, where the response should occur,would be statistically similar to the average noise power across theneighboring frequencies. An F-ratio may be used to estimate theprobability that the amplitude at the modulation frequency in theresulting FFT is not statistically different from the EEG-noise levelestimate. When this probability is less than 0.05 (i.e. p<0.05), theSS-AEP response may be considered significantly different from noise,and the patient 60 is considered to have heard the SS-AEP stimulus. Amore stringent criteria, such as p<0.01 can also be chosen. Theobjective audiometric test system 10 can provide a probability for eachSS-AEP response based upon the statistics such as F-Ratio and phasecoherence. In the case of R-AEP responses, these types of statistics canbe applied to spectrogram data which is generated from the signalanalysis module 46, using techniques which will be described.

Referring now to the detection module 50, a phase weighted t-test may beused to detect the presence of SS-AEP responses in the recorded EEG data(Picton et al 2001, PCT/CA 01/00715). The phase weighted t-test employsdata biasing to detect the SS-AEP response based on a priori knowledgeabout the SS-AEP response. As described in the prior art, severalapproaches can be used to define the expected phase. First and foremost,the database 52 contains normative or “expected” phase values. Othermethods are also described for choosing correct reference phase values.The detection module 50 may be adapted to perform other statisticalmethods for detection, such as the MRC method. The use of an expectedphase angle has been incorporated as a variant of the Rayleigh test forcircular uniformity (RC) termed the modified Rayleigh test (MRC). The RCmethod can be made more statistically powerful if an expected phaseangle is known. Probabilities for these two types of tests are computedusing critical values available in standard statistical referencematerials (e.g. Zar, 1999).

Audiometric Test Methods

Several novel tests and related methods will be described, all of whichoffer advantages over the known art. The first tests describe RapidSS-AEP Screening Tests. The second tests are for rapid screening usingthe Conditional MASTER Screening Test, which provides somefrequency-specific information. The third tests are for rapid screeningusing the Single-Modulation-Frequency Test, which also provides somefrequency specific information. The fourth tests are for obtainingR-AEPs and utilize ramping stimuli to carry out Ramping StimuliTechniques for Screening and Threshold Tests. A number of embodiments ofthe Ramping Stimuli Techniques are described such as the DynamicIterative Ramping Test, and the Fractionated Iterative Ramping Test,each of which offer advantages not found in the prior art. The fifthtype of tests can be used to build a modulation transfer function of theauditory system and detect best modulation rates using ModulationOptimization Tests (MOT). The sixth type of tests evaluate some of theprocessing abilities of the auditory system using the R-AEPFine-Structure Tests and the RAMPER Masking Tests. Each of these testscan be realized in a software module which resides within the auditoryevaluation program 40.

Rapid SS-AEP Screening Tests

In one preferred embodiment, an SS-AEP rapid screening test isaccomplished by recording the SS-AEP to at least one amplitude modulatednoise (e.g., white noise with band-pass 1-8 kHz) stimulus which ispresented to at least one ear of a patient. The inventor has discoveredthat noise stimuli reliably and rapidly evoke significant SS-AEPs inboth infants and adults, which are considerably more robust than themodulated tonal stimuli normally used in frequency specific auditorytests (John et al., 2003a). For example, the experimental results ofFIG. 2 provide evidence that this increase in amplitude enables therapid detection times required in screening tests. Since the filing ofthe provisional application on which this application claims priority,the inventor has been able to test low intensity stimuli, and show in agroup of young infants, that this method works well as a screening test(John et. al, 2003b). Repetition/modulation rates should be chosen tocreate large SS-AEPs that can be more easily detected in the frequencydomain. For adults, the repetition/modulation rates preferably should beabove approximately 20 Hz and usually less than approximately 300 Hz,while for infants, the repetition/modulation rates preferably should beabove approximately 70 Hz and also less than approximately 300 Hz. TheMOT, described later in this application can be used to pick the optimalmodulation frequencies to perform screening tests.

FIG. 2 shows data from one adult subject and demonstrates thefeasibility of a screening protocol. The responses to the left ear andright ear stimulus appear at 85 and 95 Hz, respectively, which were therates at which the noise stimuli (1 Hz-8 kHz) were modulated. Thestimuli were presented at an intensity of 50 dB SPL, which is about 25dB above normal hearing threshold. The responses were assessed after 8seconds and after 16 seconds. The graphs plot a portion of the amplitudespectrum near the frequencies at which the responses appear. Eachresponse (arrowheads) can be considered statistically present at thefrequency of stimulus-modulation if the amplitude of the response isstatistically larger than the background EEG-noise levels. In thissubject, the responses were statistically significantly different fromnoise at 8 seconds (open arrowheads) and were highly significant andvisibly larger than background noise by 16 seconds (filled arrowheads).The fact that these responses were so large, compared to EEG-noiselevels, using relatively low stimulus intensity levels, and becamesignificant so quickly was unexpected, and, accordingly, the inventortested other subjects to see if this effect was reproducible. Theinventor was able to elicit these types of robust responses in allsubjects that were tested as is shown in FIG. 3.

FIG. 3 shows the distribution of the times required before the responsesin 10 subjects (20 ears) were significantly different from EEG-noiselevel estimates. Seventy-five percent of the responses were significantby one minute, and all were significant before 3 minutes, demonstratingthe promise of the procedure as a very rapid screening test. Theseresults have been replicated on other subjects and other types ofstimuli have been tested as well. For example, since the time of filingof the provisional application, the inventor has also shown that thistechnique works well in infants, with 100% of the infants who weretested passing within 43 seconds, using non-conservative statisticalcriteria and a p-value of 0.05 (John, Brown, Muir, and Picton, 2003b)

The inventor has also demonstrated, using adult subjects, that severalnovel stimuli may work better than amplitude modulated broadband noise(BBN) stimuli (band-pass 1-Hz to 8 kHz) when used in a rapid screeningtest. While the response to a BBN stimulus was 77 nV, larger responseswere obtained when the modulated noise did not contain lowerfrequencies. Using a modulated high-pass noise (HPN) stimulus (e.g., 2kHz to 8 kHz), SS-AEP amplitude increased to 89 nV. Further, theInventor demonstrated that SS-AEPs can be augmented by simultaneouslypresenting low frequency sound. When a modulated HPN stimulus waspresented simultaneously with a modulated low-pass noise (LPN) stimulus,the response to the HPN stimulus increased to 94 nV. This type ofenhanced HPN stimulus is called an EHPN stimulus. Accordingly, BBN, HPN,and EHPN stimuli can all work well for screening. Variations of thesestimuli are possible. For example, a different range for the frequenciesof the HPN (e.g., 1.5 kHz to 7 kHz) may be used, and the LPN stimuluscan be modulated or un-modulated.

The use of amplitude modulated noises for screening is not known perhapsbecause the size of the responses (and their SNR) at low intensitylevels and the resulting rapid time-course needed to becomestatistically present has never been investigated, and it was notrealized that these stimuli could be advantageous in providing a rapidscreening test with good specificity and sensitivity. Until the inventorconducted experiments which led to a recent study (John et al, 2003a) itmay not have been understood that the response to noise stimuli were ofsufficient magnitude to cause the SS-AEPs to become significant withinthe very short time needed for a screening test. (e.g., within 3 minutesfor all the subjects tested). Although the stimuli used in this studywere slightly higher in intensity than might be used in a screeningtest, the data indicated that presenting these stimuli at slightly lowerintensities would still enable a screening test to be clinically useful.The average amount of time for most of the responses to becomesignificant was between 30 and 60 seconds for different stimuli tested,with the maximum time being no longer than 2 minutes, and no testedsubjects with normal hearing failed to produce a significant response.

Rather than using modulated steady-state stimuli, transient stimulipresented at rapid rates can also produce SS-AEPs. However, in order forthe frequency analysis, and other aspects of data processing, to workaccurately and efficiently, the transient stimuli should occur atintervals that are equal to integer sub-multiples of the DA and ADbuffers. When both ears are tested at the same time, the repetition ratefor one ear also should occur at a different rate than that used in theother ear. An example of this method is as follows. The number of pointsin the DA buffer is made equal to the product of the integer numbers ofcycles of the two stimuli within a single epoch multiplied by a power of2 (in order to produce approximately the same number of points as the DArate, e.g. 32,000 data points, so that epochs are about 1 second each).A further proviso, that the AD buffer is a sub-multiple, e.g., 1/32, ofthe DA buffer. This is ensured by choosing the two rates so that thefinal number of AD-buffer points is divisible without remainder by thenumber of DA-buffer points (i.e., in this case divisible by 32). Forexample, the two modulation rates can be 90 and 96 cycles-per-epochwhich will result in a product of 8640. This value is then multiplied by4 to give 34,560 points. This result is then divided by 32, without aremainder, in order to obtain the number of points (1080) that are ineach AD buffer. Because the A/D rate is set at 1000 Hz and the AIDbuffer contains 1080 points, the epoch duration is 1080 ms and theactual frequencies for the two stimuli are 83.33 Hz [i.e.,90*(1000/1080))] and 88.89 Hz, respectively. Both the A/D rate of 1000Hz and the D/A rate of 32,000 Hz are acceptable since these are bothinteger submultiples of the clock (e.g., a 20 MHz clock), used by thedata acquisition board of the data acquisition system 14.

The inventor recently tested several types of stimuli, which wereadjusted to have approximately the same intensity relative to asubject's behavioral threshold (i.e. nHL). The first stimulus was theBBN stimulus. The next two stimuli were condensation clicks (CC) andrarefaction clicks (RC), lasting 125 msec. The remaining stimuli were 1ms tone-bursts with instantaneous rise and fall times. These burstscontained BBN or a tone (e.g. 1400 Hz). The average response amplitudefor the BBN stimulus was 90 nV, for the CC stimulus was 129 nV and forthe RC stimulus was 137 nV. The burst-BBN and burst-tone stimuliproduced response of 126 and 149 nV, respectively. All of thesetransient stimuli can be used in rapid hearing screening tests.

When used as a screening test, the stimuli are presented (to one or bothears) at a single level and the subject receives a pass/fail resultdepending upon whether the responses are statistically determined to bepresent. Alternatively, the stimuli can be sequentially presented at 2or more levels. The lowest level at which a response occurs for aparticular ear is the threshold for that subject in that ear. The testmay be repeated 2 or 3 times in order to ensure the reliability of theresults. Additionally, since the overall amount of background EEG-noisewill affect the SNR level, and accordingly, response detection, an EEGnoise-level criterion can be imposed where the test continues until theamount of background noise is below a specified level. The EEGnoise-level criteria can be based upon normative population values andstored in a database.

In an alternative embodiment, because an SS-AEP stimulus presented at 65dB SPL should become significant almost immediately, a method ofperforming a screening test can be done at two or more intensity levelswhereby the SS-AEP stimulus is first tested briefly, e.g, 10-30 seconds,at a high intensity level, e.g., 65 dB SPL, prior to testing at normalscreening intensity levels, e.g., 45, dB SPL. If the individual failsthe higher intensity level, then an immediate Fail result is issued, aslong as the EEG-noise levels are below a specified level which can beobtained from the database, without spending 1-5 minutes testing at thenormal screening intensity level.

If there is too much background EEG-noise in the recorded data, asubject may not show significant responses even though hearing isnormal. One method of determining the acceptable amount of backgroundEEG-noise (“noise level criteria”) is to use normative data (either aself norm or population norm or combination of the two), whereby thenoise estimate must be below a specified value in order for the test tobe regarded as valid. Noise level criteria may be obtained from thedatabase 52 and can be applied to all the auditory tests describedherein. The EEG noise-level criteria can vary with test protocol andstimulus characteristics (e.g., intensity of the stimuli being tested),characteristics of the noise estimate (e.g., band pass of frequenciesused by the estimate), and may vary with test protocol. A method ofgenerating a self-norm for EEG-noise level criteria is to use an earlysample of the response data, or data from a previous recording which mayhave been made using a different stimulus in order to determine what theEEG-noise level criteria should be for that subject. Only if theEEG-noise level estimate reaches one or more of the specifiednoise-level criteria is the test deemed to be acceptable. Accordingly,noise-level criteria can be used in part, or as the sole determinant, todetermine whether a screening test (or testing at a specific intensityin the case of a threshold test) should either continue or be halted.When recordings have too much noise, the auditory evaluation program 40displays a message that the test results are not valid. For example, awarning signal such as, “Too much noise to perform test accurately” canbe displayed.

If the energy in the EEG-noise occurs at the same frequency as theSS-AEP or R-AEP being measured, then the test may indicate that thesubject can hear a stimulus, even though this is not the case. This canoccur when energy in the background EEG noise occurs at, or leaks into,a frequency bin which is used to measure an evoked response. Homogeneitycriteria are therefore important in suppressing false positive or“spurious” results (i.e., an evoked response is determined to bestatistically present when in fact it does not exist). For example, ifthe energy in an epoch is statistically different than for all otherepochs (i.e., with respect to total energy, energy in a high frequencyor low frequency band, or energy in the frequency bin which correspondsto the evoked response) then this would likely not occurphysiologically. Accordingly, using homogeneity criteria will decreasespurious responses because these criteria decrease the chance thatspectral energy, which is not related to the evoked response, will showup spuriously in the frequency bins used to measure the energy of theevoked responses.

By measuring the amplitude of the EEG-noise levels for each of theepochs that are collected, it is possible to determine if one epoch hasa much larger EEG-noise level or has much larger energy over a specifiedfrequency range, or within a single bin of the amplitude spectrum, thanthe other epochs. Homogeneity criteria may be adjusted based upon theepochs, or a subset of the epochs (e.g., the set of epochs correspondingto the first 2 minutes of data) that are already collected for asubject. Homogeneity criteria can specify that, for example, in orderfor each epoch to be accepted, a characteristic (e.g., the energy from 1to 40 Hz) of that epoch must not exceed a criteria (e.g., must notexceed 2 standard deviations above the mean value as calculated from theother epochs of the data set). As new data epochs are collected, it ispossible to dynamically update the homogeneity criteria that are used toreject epochs that do not meet these criteria. In other words, as thetest progresses and more data are collected the homogeneity criteria maychange since the mean and standard deviations of various measures wouldchange. Accordingly, in one embodiment, epochs that were previouslyaccepted, may become rejected, and vice-versa. Homogeneity criteria mayalso be based upon normative data obtained from the database. Forexample, the criteria can be based upon data obtained during a previoustest, (e.g., at a higher intensity) for that subject (i.e. a self norm).Homogeneity criteria can be adjusted and applied to the evoked responsedata continuously, at the end of each sweep, in response to certainevents which occur during testing (the probability of the presence ofone or more responses changes by a large amount very rapidly), inresponse to a user's request, or periodically. Homogeneity criteria canbe applied at the end of an evoked response testing procedure to ensurethe integrity of the results.

Homogeneity criteria can also be created for SNR levels, for example,they can rely upon the mean and standard deviations of an SNR estimatebased upon the amplitude of an SS-AEP compared to the amplitude of anoise estimate. As is well known to those in the art, because somefrequency domain techniques result in spectral estimation which isrelated to the length of data which are submitted (e.g., FFT), theamount of energy in the bins of an epoch can be analyzed usingzero-padding techniques (e.g., a series of zeros corresponding to theamount of data which would exist in 15 epochs of evoked response dataare appended to each epoch which is to be evaluated). In the case ofadaptive filtering techniques, such as Kalman filtering, this may not bean issue, since an accurate estimate of the signal amplitude can beobtained with very little data.

Determining if the response is statistically present may be approachedin several manners. One method is to determine the average amount oftime needed for the responses of a typical subject to reach significance(i.e., the test relies upon population normative data for test time).The typical testing duration can then be limited to this time period andat the end of the test, the evoked response data are evaluated. Theevoked responses which not have reached significance produce a “fail”test result. Using population normative data, the maximum test durationcan be set equal to the amount of time required for some portion, e.g.,95%, of the population to reach significance. This use of a maximum testperiod thereby limits the duration of a test. Instead of repeatedlytesting an evoked response, the use of a maximum test period can be usedto test the evoked response only once and avoid statistical issues ofmultiple comparisons. Further, some evoked responses should not becomesignificant prior to a minimum amount of time. Evoked responses whichbecome significant prior to this time are probably due to noise enteringthe frequency bin of the evoked response. Accordingly, use of a minimumtest period may increase the duration of a test because, even if theevoked responses become significant immediately, use of a minimum testduration requires the test to continue, however there will be lessspurious responses.

Alternatively, the probability of the presence of the evoked responsescan be evaluated sequentially, after each data sweep is collected(“sequential response testing”), in order to produce a “significanceseries”. A significance series may be generated as follows. As eachsweep is collected, the average sweep is evaluated and the significancelevel of any responses being evaluated is plotted as a function ofcumulative sweep number. Because responses can become significant inless than 16 seconds, and because using a 16 second sweep only permitsevaluation of the data in 16 second intervals, the sweep can beshortened to contain only 1 or 4 epochs. Accordingly, a data sweep maybe 1.024 seconds in length, or may be longer or shorter. Sequentialresponse testing may lead to a shorter test times, than using a fixedduration for the testing procedure, but may lead to an increase in thenumber of false positives (i.e., spurious responses) because, as thenumber of statistical tests carried out increases, the chance of findingsignificant results increases.

FIG. 4 a shows an example of sequential response testing of asignificance series in a subject presented with two 30 dB SPL modulatedBBN stimuli (one to each ear) that were about 5 dB above the subject'sbehavioral threshold (the SS-AEP to only one of the two stimuli isshown). FIG. 4 b shows sequential response testing in a subjectpresented with 20 dB SPL modulated BBN stimuli that were about 5 dBbelow the subject's behavioral threshold, and which should therefore notevoke a statistically present response. The data were generated bycollecting 220 sweeps of 4-seconds each and evaluating the data (usingthe F-Ratio) to determine if a response was present at the 0.05significance level after each sweep was collected. Below each figure isa series of numbers. The upper row contains points where the responsetransitioned from being non-significant to significant at the 0.05 level(i.e., “0.05 Cross (dN)). The lower row contains points where theresponse transitioned from being significant to non-significant at the0.05 level. For the stimulus which should have been heard, the averagedata made from iteratively adding the current sweep to a running averagesweep, indicates that from 8 to 12, 14 to 15, 17 to 19, and then from 20onward the response was statistically present at the 0.05 level ofsignificance. In FIG. 4B, the significance series reaches the 0.05 levelbriefly (for only 1 or 2 sweeps) near the middle of the test period.Later on there are two sections of the significance series where thesignificance criteria is met for a longer period, such as from 163 to171, which is a span of 8 sweeps (i.e., about 32 seconds).

Accordingly, while the data in FIG. 4 a reach significance quickly(within 21 sweeps or 84 seconds), the data in FIG. 4 b also becomesignificant for a limited period towards the end of the recording. Iftesting was simply halted when the response reached significance thenthe data in 4 b would yield a “false positive” result, suggesting thatthe subject heard the modulated signal, when in fact this was unlikely.This type of false positive evaluation would be expected to occur due tothe use sequential response testing, which relies upon multiplecomparisons.

The problem of multiple comparisons can be countered using techniqueswhich incorporate “statistical conditional criteria” (SCC). Onetechnique, termed the “absolute count”, uses a SCC in which a specificnumber of sweeps must reach significance before the response isconsidered significant. In other words, the response must be evaluatedas significant for a specified number of sweeps. Using an absolute countSCC whereby 40 points must be significant would result in the data ofFIG. 4 b being correctly classified as a “response absent” result (i.e.,“Fail”). Another SCC technique, termed the “sequential count”, requiresthat a response must demonstrate significance across a specified numberof consecutive sweeps before the response is considered significant. Inother words, the response must remain significant for a specified numberof sweeps. Using a sequential count criteria whereby a response mustremain significant for 5 consecutive sweeps would result in the data ofFIG. 4 b being classified as a “response absent” result. Anothertechnique, termed the “relative count”, requires that the ratio of thenumber of sweeps that reach significance divided by the number of sweepsthat did not reach significance must be above some value before theresponse is considered significant. Using a relative count criteriawhereby 80% of the total number of points must be significant would alsoresult in the data of FIG. 4 b being classified as a “response absent”result. The absolute, relative, and sequential count criteria can beapplied to the entire significance series, or may be applied to 2 or 3subsections of the series. Each subsection can continue to analyze anaverage sweep, or each subsection may contain a significance serieswhich is computed upon average sweep which is based only upon the datawhich corresponds to the time over which that subsection is beingevaluated (i.e. separate significance series are generated for 2 or 3average sweeps which are created from 2 or 3 sections of the data). Theresults of the SCC which are applied to each section can be combined todetermine whether a response is statistically present.

Several criteria, such as SCC, may be combined. For example, therelative count criteria technique could be combined with a criteria thatdictates that a minimum number (e.g., 30) of sweeps (i.e., a minimumtest period criteria of about 120 seconds) are required prior toallowing the application of SCC to the significance series. In FIGS. 4 aand 4 b, significance series and SCC, could have been generated with a0.01 critical significance value rather than a 0.05 criticalsignificance value. Further, the critical value relied upon does nothave to be constant, but can change as the recording period progresses.For example, the critical value could reflect the 0.05 level as it isadjusted using the Bonferroni method, whereby the significance level isdecreased to increasing stringent probability levels as more comparisonsare made. The SCC values can be obtained from the database 52, for thedifferent test protocols used.

By collecting data on a large normal population for a test protocol, thenormative values for absolute count, absolute consecutive count, andrelative count can be computed using critical values of 0.05, 0.01, orother critical values so that these SCC yield the desired levels offalse positives and false negatives. For example, ROC analysis can begenerated as, is well known to those in the art, in order to evaluatethe accuracy of using various SCC. The number of false positives can bemeasured by evaluating frequency bins, which do not correspond to amodulation/repetition rate of a stimulus being tested, and computing howmany are statistically present (e.g., counting how many bins aresignificantly larger than the noise estimate). The number of falsenegatives can be measured by evaluating frequency bins that correspondto a modulation/repetition rate of a stimulus being tested, andcomputing how many are statistically absent although they should bepresent (e.g., counting how many bins fail to be significantly largerthan the noise estimate). The appropriate values for various SCC canalso be determined using permutation simulations, whereby data from anumber of actual subjects is re-sampled in order to create a largenumber of “virtual subjects”, on which the normative values are based.For example, 150 epochs of evoked response data are randomly chosen,from the 220 actual epochs which were collected. This process isrepeated 40 times, in order to create 40 virtual subjects. A dataset of400 subjects can be derived from 40 subjects, and normative values canbe derived. This data set can then be subjected to ROC analysis tocompute what the SCC should be to obtain a desired level of sensitivityand specificity.

The screening test described here is novel and advantageous over priorart for several reasons. For example, it uses SS-AEP stimuli to performrapid tests of hearing, which, even at threshold intensities, are shownto provide robust responses that rapidly can be shown to bestatistically present in normal hearing. The screening tests canutilize, for example, novel steady-state stimuli, such as EHPN stimuli,to generate SS-AEPs. The screening tests can also utilize speciallydesigned periodic stimuli to generate SS-AEPs. Even though some of thestimuli have some frequency specificity, the main aim of the rapidscreening technique is to provide pass/fail result for overall hearingability. However, an additional aim is to provide a rapid pass/failresult for specific frequency ranges. Two or more stimuli can bepresented simultaneously in each ear. The stimuli described here canalso be used in a rapid threshold test. The screening test also utilizesnovel methods such as the incorporation of a significance series,statistical conditional criteria, and homogeneity criteria. The use ofnoise-level criteria, homogeneity criteria, maximum and minimum testperiod criteria, and significance series with SCC are novel methodswhich can also be applied to many of the testing protocols now describedas well as to the MASTER technique of the prior art.

Rapid Screening Using the Conditional MASTER Screening Test

The rapid screening methods already described use various stimuli, thatrange in their frequency specificity, in order to obtain a quickestimate of an individual's overall hearing ability. Accordingly, whilethere was a limited frequency range in some of the stimuli described oneuse of the tests is for non-frequency specific screening or evaluationof non-frequency-specific thresholds. For example, the EHPN stimulususes two types of band-limited stimuli that are somewhat frequencyspecific to quickly obtain a simple indication that the auditory systemcould respond to sound, rather than to specifically screen forfrequency-specific hearing loss in those two frequency regions. Thesemethods of rapid SS-AEP screening are therefore similar to theclick-evoked ABR test, in that they are not intended to befrequency-specific, but the ASSR test should be faster. Since the rapidASSR screening tests, and the click-evoked ABR test which is usedcurrently for first stage of screening, are not frequency specific,subjects with good hearing over a limited frequency range, but who havefrequency-specific hearing loss, may obtain a “pass” result. However,these types of narrowband or band-limited noise stimuli can also beused, instead of, or in addition to, pure tones, in order to perform arapid frequency-specific screening test.

By using the MASTER technique to test hearing at both low and highfrequencies, Perez-Abalo et al. (2001) suggested that ASSR's could beused as a rapid frequency specific screening test. They used the 500 Hzresponse and the 2000 Hz response (both stimuli presented at 50 HL) intheir “screening” procedure and found that they could perform‘screening’ similar to the click-ABR test. The use of noise-levelcriteria, homogeneity criteria, maximum and minimum test periodcriteria, and significance series and SCC can be incorporated into theirtest to improve its performance. Additionally, the Conditional MASTERScreening test, can be used as a rapid frequency-specific screening testwhich can be faster and more reliable than this prior art. For example,in a Conditional MASTER Screening test, the MASTER test is used withthree or more stimuli, and a criteria for passing can be set where, forexample, if any specific number, such as any 2 or any 3 of the SS-AEPs,reach significance, then the subject will pass the test. This is animprovement over the prior art because, rather than requiring the 500and 2000 Hz responses to reach significance, the subject will passregardless of which 2 (or 3) responses become significant. For example,if either the 500 and 1000, or 1000 and 4000, or other combination of 2responses (or 3 responses, if that is the pass criteria) becomessignificant, the subject will have been deemed to pass the test.

This type of alternative test is a compromise between having a frequencyspecific threshold test, and a non-frequency-specific test that iscurrently provided in the click-ABR test. The Conditional MASTERScreening test can be accomplished using either pure tone stimuli, orband-limited noise (including narrow band noise), as the carriersignals. The modulation envelopes effect 100% modulation for themodulated stimulus, and can be modulated at rates of betweenapproximately 30 Hz and 300 Hz. Because the thresholds for differentregions of the cochlea may be different, the individually modulatedstimuli can each be adjusted to an appropriate intensity prior to beingcombined into the MASTER stimulus (i.e., the four stimuli can each havedifferent intensities).

The Conditional MASTER Screening tests have some statistical concernsbecause responses may become significant by chance for a short period oftime, due to energy which happens to be in the bins associated with themodulation frequencies, and is merely due to chance. Accordingly,noise-level criteria, homogeneity criteria, maximum and minimum testperiod criteria, and significance series and SCC can be used. The SCCcan be applied across multiple stimuli. For example, in one embodiment,at least a specified number of SS-AEPs must become significant and staysignificant, according to a specified critical value of significancesuch as p<0.05, for a specified number of sweeps (specified usingsequential count SCC) in order for these responses to be consideredstatistically present and for a “Pass” result to occur.

In one embodiment, a method of performing a rapid auditory screeningtest according to Conditional MASTER Screening Test comprises:

a. presenting at least three modulated acoustic stimuli to at least oneear of a subject;

b. recording evoked response data which is organized into data epochs;

c. classifying each of the data epochs into accepted epochs and rejectedepochs;

d. processing the accepted epochs to determine which SS-AEPs arestatistically present;

e. repeating steps a-d until at least one specified criteria has beenmet such as a minimum test period criteria, a minimum noise levelcriteria, all of which can be based upon normative data which isobtained from the normative database; and,

f. providing a pass result if at least a specified number of SS-AEPswere statistically present and a fail result if less than a specifiednumber of SS-AEPs were statistically present.

In this embodiment of the Conditional MASTER Screening Test certainmethodology may used. For example, in order to compensate for multiplecomparisons, in step d, each SS-AEP is not statistically present until asignificance series has been generated for each SS-AEP, and this hassuccessfully passed one or more statistical conditional criteria. Inaddition to, or as an alternative to using SCC, the specified criteriaof step e, can be an amount of time or a level of background EEG-noisepresent in the recording (i.e., noise-level criteria). Theclassification of the data in step “c” can be based upon failure orsuccess of the data epoch in meeting homogeneity criteria.

An alternative embodiment of the Conditional MASTER Screening Testcomprises:

a. performing a MASTER test with at least three stimuli presented atspecified intensities;

b. stopping the MASTER test after a certain amount of time or after anoise-level criteria have been met;

c. providing a pass result if at least a specified number SS-AEPs areassessed as statistically present.

This embodiment can also utilize homogeneity criteria and statisticalconditional criteria

Single Modulation Frequency Test

An alternative screening test, which provides a compromise between afrequency-specific screening test and a rapid overall test, is theSingle Modulation Frequency (SMF) test. In the SMF test, two or morecarrier frequency stimuli are modulated at the same rate, in order toproduce a larger SS-AEP than would occur when the two carriers were eachmodulated at their own rates and produced two separate responses. In theSMF test, the failure of the SMF stimulus to reach significanceindicates a hearing deficit in one of the frequency specific areas whichis being stimulated by the frequency specific stimuli.

The SMF test can also be accomplished using a “virtual SMF stimulus”. Avirtual SMF stimulus can be created by presenting several carriers, eachmodulated at a unique modulation rate as occurs during the conventionalMASTER test. The responses are then virtually added together, andcompared to a noise estimate. In this case, the virtual SMF response canreach significance rapidly, but there is still frequency specificinformation available. If the SMF response does not quickly becomesignificant, indicating that the subject has not passed the test, thenthe SMF test is allowed to continue and the responses to each of theMASTER stimuli are evaluated as would occur during a normal MASTER test.Both the SMF and virtual SMF stimulus have been described by the author(John, Dimitrijevic, and Picton, 2003).

In one embodiment, a method of utilizing an SMF test comprises:

a. performing a MASTER test with at least 2 SS-AEP stimuli that aremodulated at the same single modulation frequency; and,

b. analyzing the resulting SS-AEP data to determine a pass or failresult.

In another embodiment, a method of utilizing an SMF test comprises:

a. performing a MASTER test with at least 2 SS-AEP stimuli with uniquecarrier and modulation frequencies;

b. processing the resulting SS-AEP data to derive a virtual SMFresponse; and,

c. determining if the SMF response is statistically present in order togenerate a pass or fail result.

Methods for R-AEP Screening Tests and R-AEP Threshold Tests

Stimuli

In one embodiment, a method is used to achieve rapid estimate of asubject's hearing threshold, whereby ramping evoked potentials, or“R-AEPs”, are evoked by a ramping intensity stimulus. Various functionsmay serve as the ramping envelope. Because intensity is measured upon alog scale, using decibel (dB) units, a ramp which has a linear growthfunction when plotted in dB units is one of several appropriate rampfunctions that can be used. Because any large jump which occurs in thestimulus intensity, or other characteristic of the ramping stimulus, maybe startling to a subject, it may be preferable to use a symmetricalramping function which consists of a first half that increases inintensity, followed by a second half that decreases in intensity. Thelast part of the stimulus can be presented at an intensity which isequal to that of the first part of a subsequent stimulus so that nodiscontinuities are experienced by the subject. Symmetrical rampingfunctions thereby avoid the large changes in stimulus intensity thatwould exist if a simple continuous increase or decrease of intensitywere used. However, either one-sided or symmetrical or non-symmetricalramp functions can be used.

One method of creating a ramping stimulus, comprises digitallymultiplying a ramping function with a base signal. A base signal can beconventional types of modulated, steady-state, or periodic stimuli.Repetition/modulation rates of the base signal should be fast enough tocreate oscillatory ramping responses that can be evaluated bytime-frequency analysis. In adults this should be above approximately 30Hz and usually less than approximately 300 Hz, while in infants thisshould be above approximately 70 Hz and less than approximately 300 HzAn example of creating an intensity ramping stimulus is now provided.The base signal, such as an amplitude modulated BBN stimulus, shouldrange between −1 and 1 (depending upon the range of the D/A buffer ofthe data acquisition card, these values can be in volts or can be inarbitrary units, which are later adjusted to utilize the full range ofthe card). In a programming language called LabVIEW, the broadband noisecan be created with the subroutine called “uniform white noise.vi”,which is then modulated at a particular frequency. After the base signalis created it is multiplied with the ramping function in order to createa ramping stimulus. The ramping function preferably ranges between 1 andzero. When the instantaneous value of the ramping function is 1, theramping stimulus will therefore have its maximum intensity. The maximumintensity is equal to 1, with the decrease in intensity being determinedby the equations:

I _(t)=10̂(−t*C)

where I_(t) is the SPL level at a given time-point, “t” is the currenttime-point and C is a constant that is equivalent to the intensity stepwith C being defined by

C=R/(N*20)

where R is the desired range of intensity in dB, and N is the number ofpoints in the ramp function. Accordingly, if R is set to 10 and the basesignal has an amplitude which will produce an intensity of 50 dB SPLthen the ramped stimulus will decrease from 50 to 40 dB SPL. An upwardramp can be generated by taking the original ramp function and reversingthe order of the points. By setting N to ½ of the total number points ofthe desired stimulus length, and then reversing the original functionand adding this result to the original function, a ramp stimulus of Nlength can be created which contains both an upward and downward goingramp (i.e., a symmetrical ramp). Other types of ramping functions canalso be used, including linear, non-linear, and multiple slopefunctions. For example, a ramp can contain two slopes with the firstslope being relatively shallow and lasting for 80% of an upward ramp,while the remaining 20% of the upward ramp has a steeper slope. This maybe useful since R-AEPs evoked by lower intensity stimuli have a lowerSNRs than R-AEPs evoked by higher intensity stimuli. Accordingly, moretime is spent obtaining a stable estimate of R-AEPs with lower SNRlevels so that these are estimated with a more similar degree ofaccuracy as the R-AEPs obtained to higher intensity stimuli. A rampingstimulus can also be created by analog means whereby the intensity of aprogrammable audio amplifier is dynamically adjusted according to theramping function.

When the R-AEP test involves creating a R-AEP stimulus which varies inintensity, then the ramping function can be multiplied with the basesignal as just described. As will be described in other methodsdisclosed herein, the R-AEP stimulus can change for a differentattribute than intensity, such as modulation rate (e.g., ramping fromlower to higher modulation rates over time in one case), carrierfrequency (e.g., ramping from lower to higher carrier frequencies overtime, while holding modulation rate constant in one case), stimulus type(e.g., ramping from a broadband to narrow-band carrier signal overtime), or for a different feature of the stimulus. The creation oframping stimuli, which are not intensity ramping stimuli, does notalways entail multiplying a base signal with a ramping function, butrather, is accomplished using more complex mathematical equations, wherethe quality of the stimulus which is to be ramped, changes appropriatelywith time. For example, the function in MATLAB called “chirp” may beused to generate ramping stimuli. The function enables parameters of astimulus which ramps in instantaneous frequency to be easily set. Thefunction permits the setting of the initial frequency, final frequency,time of ramp, initial phase of ramping frequency, the shape of the ramp(e.g., determines whether the sweep frequencies vary linearly,quadratically, or logarithmically), whether the ramp is positive,negative, or unidirectional/symmetrical (bidirectional), as well asother features of the ramping function. When the ramping stimulus is toramp across carrier frequency, the output of the chirp function is usedto create the carrier signal. When the ramping stimulus is to vary inits modulation rate (e.g., as occurs in the MOT), the output of thechirp function is used to create the modulation function. Alternatively,a standard equation such asramp_(i)=A_(i)*sin(2*p_(i)f_(i)*t_(i)+PHI_(i)) can be used to createeither the carrier or modulation function of a ramping stimulus, whereA_(i)=the instantaneous amplitude, Ramp_(i)=the instantaneous value ofthe ramping stimulus, f_(i)=instantaneous frequency, t_(i)=the time, andis the instantaneous phase of the stimulus. Some methods of creatingmodulated stimuli have been described by the inventor (John & Picton,2000). Like the ramping intensity stimulus, because the attribute of thestimulus which is being ramped will have specific values at any momentin time, using time-frequency analysis of the R-AEP data enables theevaluation of the response to the stimulus at any given point in time.

General Method

An example of performing a ramped intensity test is shown in FIG. 5, andis reflective of what the Inventor has used in some initial studies withthe method. The first panel of the figure shows the intensity of theramp stimulus, which increased from 20 to 50 db SPL during the first ½of the data sweep (8.192 seconds) and then decreased for the secondhalf. This is a symmetrical ramp stimulus. As can be seen in the secondfigure panel, the recorded EEG data were stored in 256 epochs (16 sweepsof 16 epochs each), lasting 1.024 seconds each. The contiguous epochswere concatenated into 16.384 sec. sweeps which are shown as rows in thefigure panel and were averaged together in the time domain (each columnis averaged together). In this example, 16 sweeps were collected,causing the recording to last about 4 minutes. The third figure panel,shows the resulting 16.384 sec. averaged sweep, which will containresponses that occurred over the entire range of the ramping stimulus.Accordingly, while the evoked response data are still collected in dataepochs, the ramping stimulus spans across the entire sweep.

The averaged sweep may be analyzed in several manners in order to obtainthe time-frequency information. For example, a spectrogram can becreated by using a moving window of, for example, 1024 points, with anoverlap of 1000 points (i.e., the window is iteratively shifted throughthe data by 24 points). The entire 16,384 point waveform can be analyzedin 682 (16384/24) separate FFT's which will yield a spectrogram. Such aspectrogram is shown in the next panel of FIG. 5. The responses tostimuli presented at 85 Hz (left ear) and 95 Hz (right ear) can be seenas 2 horizontal lines towards the top of the spectrogram which aredistinctly larger than the background noise (the larger the amplitude,the lighter the color on the plot). A fuzzy line also appears at 60 Hzdue to line noise in the measurement environment. By extracting the rowequivalent to the frequency of the modulated stimulus (or repetitionrate if transient stimuli are used), one obtains the amplitude of theresponse over time. The amplitude and phase plots of the responses tostimuli presented to the left ear (85 Hz) can be seen, as a function oftime, in the next two panels of FIG. 5. As the intensity of the ramp isincreased, the amplitude of the response increases and the phase valuesstabilize rather than being random.

The R-AEPs can be statistically evaluated to determine if a response ispresent by comparing the R-AEP amplitude, at any moment in time, to anoise estimate. For example, by extracting amplitude values of the rows(i.e. neighboring frequencies) of the spectrogram which are adjacent torow of the spectrogram for the frequency of modulation for the R-AEPstimulus (or the repetition rate for transient R-AEP stimuli), anestimate of the background EEG-noise (over time) can be obtained. Thisestimate of background EEG-noise can be used in order to carry out an Ftest. In this illustrative example, 10 rows above and below the rowscorresponding to the frequencies of modulation (for the multiple rampingstimuli) are used in the noise estimate, and the R-AEP estimates, atdifferent moments of time, are compared to the noise-estimate using anF-test with 2 and 19 degrees of freedom. In this example, the amplitudes(and phases) of the R-AEPs at different moments in time are compared toan EEG-noise estimate which was equal to the average EEG-Noise acrossthe whole sweep (i.e., the amplitudes of the noise were collapsed acrosstime). Alternatively, the EEG-noise estimates at different moments intime can be used in the calculation of the F-statistic. Similar to theMASTER technique, when generating the EEG-noise estimate, the rowscorresponding to the frequencies of the other R-AEP stimuli are skipped.In the amplitude plot shown in FIG. 5, both the amplitude of the R-AEPover time and the average level of background noise (across the entireaveraged sweep) are shown.

In order to estimate intensity thresholds for the ramping stimulipresented to the subject, the information in the amplitude plot, phaseplot, or a mixture of the two types of information can be used, as isknown in the art. The estimation of threshold can be based upon the rawdata, or can be obtained using regression. If a threshold is estimatedfrom the raw data then threshold can be defined as the point at whichthe amplitude of the R-AEP is not significantly different than theEEG-noise estimate. Since the intensity of the ramped stimulus is knownat every point in time, it is simple to calculate what the intensity ofthe intensity ramped stimulus was when the R-AEP failed to besignificant. For example, if the R-AEPs begin to be statisticallypresent at 4 seconds, then the threshold can be calculated to be about35 dB SPL (the slope of the intensity function in first panel of FIG. 5is 30 dB range over 8 sec or 3.75 dB/sec). Since there is a 1 secondwindow, the range for that data is 35+/−1.875 dB. Accordingly, thethreshold can be estimated as between about 27 and 32 dB SPL.Alternatively, if physiological threshold is thought to occur at 10 dBabove behavioral threshold, then the threshold could be calculated,incorporating this correction factor, as 17 to 22 dB SPL. As in a MASTERtest, the correction factors can be specified for different carrierfrequencies, ages, and stimuli.

Smoothing of the amplitude or phase plots may occur prior to estimationof threshold. Other types of signal processing can be used upon theamplitude and phase plots, or on the actual spectrogram as well. Forexample, an estimate of a patient's threshold for a ramping stimulus canbe based upon the lowest intensity for which the R-AEP amplitudes arenot statistically different than an EEG-noise level estimate, or can bebased upon the lowest intensity for which the R-AEP phases are notstatistically stable, may utilize regression techniques which can beapplied only to R-AEP amplitudes (which are statistically present) orphases (which are statistically stable), and the regression techniquescan applied only to R-AEP amplitudes which occurred in over a limitedtime period or intensity range of the stimulus. For example, usingregression, only data obtained when the ramping stimulus is in thehigher intensity range can be used in the estimate of threshold. As isknown to those skilled in the art, the epoch length, the sweep length,the size of the moving window, and the number of points in the overlapcan be changed without significantly deviating from the methodsdescribed in this patent specification.

Symmetrical ramp stimuli can provide 2 estimates of threshold, basedupon the data from the upward ramp and the data from the downward ramp.Alternatively, since the data are symmetrical around the maximum of theramp, the amplitude and phase data from the second half of the test canbe re-sorted in reverse, and added to the data obtained for the upwardramp to obtain mean values. If the ramping responses evoked by thedownward ramp were different than those obtained to the upward slope dueto, for example, louder stimuli being presented just prior to a softerstimuli (e.g., as may occur due to masking or hysteresis) then the firstand second halves of the ramping results would be different, but initialwork by the inventor has not found this to be the case. Computing themean amplitude from the first and second halves of the data gives a morereliable estimate of amplitude at a given intensity, but would not actto reduce the overall level of the EEG-noise floor. By obtaining themean of the complex spectra from two data windows rather than merely thetwo amplitude values, better estimates can be obtained for both thesignal and noise bins since the phases of the energy are taken intoaccount. In order to average the complex spectra, the data window shouldstart at the same time for epochs on both sides of the ramp. This can bedone by using integer sub-multiple of the epoch length (e.g., in 1024point epoch, the data windows must be advanced by either 32, 64, 128,etc time-points). Otherwise only real rather than complex values areused for estimating amplitude. For phase data this stipulation is notreally necessary because combining phase from slightly different areasmay only slightly affect calculations of phase stability or “coherence”.

The data in FIG. 5 can be analyzed in several ways in order to determinethe frequency-specific hearing thresholds of a subject. For example,only significant points of the time-frequency data can be used (i.e.,R-AEP amplitude values whose squared values are larger than the sum ofthe squares of the EEG-noise estimates are considered significant) toestimate threshold. A regression line can be fit to R-AEP amplitudesthat correspond to the significant points of the ascending slope of theR-AEP amplitudes. The intensity of the ramping stimulus (whichcorresponds to the values of the x-axis) at the point where theregression line intersects the x-axis is taken to be the behavioralthreshold. Additionally, when a symmetrical ramping stimulus is used, asecond regression line can be fit to amplitude values that correspond tothe significant points of the descending slope of the response function.A combination (e.g., the average) of the x-intercepts for the regressionlines fit rising and falling functions can then be used as an estimateof threshold. Alternatively, the upward and downward R-AEP data can becombined, and the result can be used to estimate the x-intercept andthereby estimate threshold.

Alternatively, rather than using an amplitude criteria that comparessignal-to-noise, the R-AEP amplitudes that are used to fit theregression lines can be selected based upon criteria that are based uponthe R-AEP phase data (e.g., slope of the phase or phase variance). Forexample, only R-AEP amplitudes having a distribution of phase valueswhich suggest that a R-AEP is statistically present can be used tocalculate threshold. Alternatively, the amplitude and phase data canboth be used to select the spectrogram data (i.e. amplitude and phasevalues of the R-AEPs) that should be used to calculate threshold (whichmay occur through regression). For example, only ranges of amplitude andphase data which indicate that an evoked response is statisticallypresent are used in a procedure which utilizes both amplitude and phaseinformation to estimate threshold.

When using FFT based techniques, or other techniques which analyze thedata by sequentially windowing sections of the data, the phase data ofthe spectrogram may be defined arbitrarily, in relation to the beginningof the current data window, rather than being defined in relation to thephase of the stimulus. While the phase of each ramping stimulus isinvariant, for example, zero at the beginning of the recorded epoch, thephase of the evoked response, as evaluated in each data window, is afunction both of the point in time of the beginning of the data windowand the modulation rate of each ramping stimulus. The phase data shouldtherefore be re-defined in relation to some common reference in order tobe meaningful. The actual phase, in relation to the beginning of thex-axis of the spectrogram, can be computed by adding the phase data ofthe spectrogram to phase values that are related to the offset of thewindow from the beginning of the spectrogram using the equation:

theta_(a)=.theta_(c)((T _(c) /L)*360

where theta_(a) is the actual phase value of the response frequencybeing measured, theta_(c) is the current phase value of the current datawindow in the spectrogram, T_(c) is cumulative time for the total numberof points that have occurred in the response data from the beginning ofthe recording and prior to the first point of the current window in thespectrogram, and L is the cycle length of the modulation frequency, orthe duration of the inter-stimulus interval (both cycle length andinter-stimulus interval can be referred to as the “stimulus period”), ofthe at least one ramping stimulus that was presented to the subject.

Data Quality Control Techniques

The use of a spectrogram to look at time-varying spectral data is knownin the art. However, certain rules should be followed when using rampstimuli tests. Because the time series data is time-locked to acontinuous stimulus, ramping stimulus methods may fail or severelyunder-perform when certain methods are not incorporated into theanalysis of the data. The following methods, such as applyinghomogeneity criteria, the zero replacement technique, the swappingreplacement technique, and the repeating replacement technique, arenovel from, and offer advantage over, the prior art. Data QualityControl Techniques ensure that the data which are analyzed are of thehigh quality leading to increased accuracy of threshold estimation orother type of audiometric evaluation. These techniques include applyinghomogeneity criteria, but this is more difficult for a ramping test thanfor an SS-AEP test. As can be seen in FIG. 5, the columns epochs withinthe sweeps are locked to a particular attribute, in this case intensityrange, of the ramping stimulus. All the epochs in the first column willhave evoked responses which where elicited by the lowest intensitystimuli, the second column contains data evoked by a higher intensity,and so forth. In conventional steady-state recording techniques an epochmay be rejected if the EEG-noise level is above some threshold value,and a subsequent epoch can be used in its place. However, if this isdone in the ramping technique, then the columns of the data matrix(i.e., the epochs in the individual sweeps) will contain R-AEPs elicitedby stimuli of many different intensity ranges, (or other characteristicof the stimulus which is ramped) and will not be sensible. Weightedaveraging can be used instead of artifact rejection somewhatsuccessfully. However, if there is a large amount of EEG-noise within anepoch, then when the epoch is multiplied by the weighting factor, theestimate of the signal will also be diminished.

Several techniques can be used with ramped stimuli to reject epochswhich have too much noise or which do not meet criteria, such ashomogeneity criteria. In the zero replacement technique, a noisy epochis replaced with zeros so that the averaged sweep is not affected bythat epoch, and the average of the column of data (e.g., epoch #2 withineach sweep) is divided by n−1 rather than n, where n is the number oftotal sweeps collected. In the swapping replacement technique, the epochwith noise is replaced by an epoch from a different sweep which is inthe same column. In the repeating replacement technique, the epoch withnoise is replaced by an epoch which is collected while a section of theramping stimulus which corresponds to the epoch which was rejected, isrepeated an extra time, for example, during the nextiteration/repetition of the ramping stimulus.

Epochs can be rejected using several criteria. Simple threshold criteriacan be used and can reject epochs based upon the absolute amplitudes ofthe time series data or the amount of acceptable high frequency energywithin in an epoch (e.g., spectral energy from 70 to 200 Hz). However,because the slope of R-AEP responses over time is used to determinethreshold, using homogeneity criteria, rather than simplethreshold-criteria may ensure that the data can be optimally evaluated.An example of homogeneity criteria is an inter-epoch noise criteria,wherein the amount of noise in any given epoch can not be more than, forexample, 200% of the average amount of noise measured in other epochs ofthat sweep. Alternatively, the inter-epoch noise criteria may be appliedacross sweeps, where as more data is collected, the average level ofnoise for that subject becomes more stable. Inter-epoch noise criteriamay be used both within sweeps, across sweeps, or both. Inter-sweepnoise criteria may also be used, where an entire sweep is rejected if ithas substantially more noise than the other sweeps.

Homogeneity criteria are more useful than merely rejecting an epoch ofdata because it has more noise than a cutoff value defined based uponnormative data. For example, while a “quiet” subject may produce datathat is well below a threshold criteria that is set based uponpopulation normative values, the intra-subject noise may still varyconsiderably (e.g., due to state of arousal) without exceedingthreshold. It should be understood that since homogeneity criteria canbe dynamically calculated based upon incoming data, that epochs whichwere originally rejected can become accepted, and vice-versa.

The stability of the R-AEPs is important in the estimation of threshold.As the intensity of the ramp stimulus decreases, the variance of theevoked responses will likely become larger (for both amplitude andphase), and the SNR will become worse. This variance will effectthreshold estimation both when it occurs using simple SNR criteria andwhen the slope of the responses is used to predict threshold, forexample, using regression or other means. Rather than using all theresponse data contained in the amplitude plots when predictingthreshold, only points in the amplitude plots which exceed a certainSNR, with respect to the noise estimate, may be used in the computationof threshold. This will tend to cause the threshold estimate to be basedupon the responses evoked by the higher intensity sections of theramping stimulus.

Alternatively, two or more amplitude plots may be obtained, byorganizing the sweeps in into two sub-averages (e.g., organizing odd andeven sweeps into two different sub-averages), and computing the resultsfor each sub-average. While these sub-average sweeps are combined into asingle average sweep upon which the threshold estimate is determined,these sub-averaged sweeps may provide estimates of the stability ofspecific regions of the data. By computing the cross-correlationbetween, for example, 1 second of data from the amplitude plots that arecomputed for the two (or more) sub-averages, only the sections of theamplitude plots which have a correlation above a certain value will beused in the final averaged sweep from which the thresholds areestimated.

In an alternative embodiment the ramping test can be used as a rapidscreening test. Although ramping stimuli tests may be used to rapidlyprovide hearing assessment for at least one stimulus at manyintensities, thereby providing threshold information, this informationcan also be used for screening purposes, by creating a minimum intensitywhich defines “normal hearing” comparing the results with this to createa pass/fail result. Further, the R-AEP data can be analyzed in a timedependent fashion. For example, the R-AEPs elicited by the higherintensity R-AEP stimuli should normally become significant within, forexample, 3-6 iterations of the ramping stimulus. If this does not occur,then a FAIL result can be issued, as long as the EEG-noise levels arebelow a specified level which can be obtained from the database, withoutwaiting to see if the R-AEPs evoked by lower intensities of the rampingstimulus will become significant at a later point in time.

Iterative Ramping Tests

The ramping tests previously described herein, and in the prior art,evaluate the threshold at the end of a recording procedure. The testingprocedure may be terminated when the SNR characteristics of the averagedwaveform reach specified criteria such as the noise falling below alevel specified within the normative database, or may be terminatedafter a certain amount of time. After the test has completed thresholdsare estimated from the final data.

This type of procedure may not provide an indication of how stable thethreshold estimate is. Further, this type of procedure may require moretime than is necessary. FIG. 6 shows an example of an iterative rampingtest performed by the auditory evaluation program 40 which offersadvantages over this procedure. In subroutine A, a ramp stimulus ispresented, the mean data are analyzed, and a threshold is computed. Insubroutine B, the threshold is added to a “threshold series”. These twosubroutines are repeated until the threshold series, or a portion of thethreshold series (e.g., the last 5 estimates) meets one or morecriteria, such as being below a specified variance estimate (e.g.,standard deviation, co-efficient of variation, or percentage change fromlast estimate). The variance estimate of the threshold series can beiteratively computed upon data such as, the cumulative averaged sweep,the individual sweeps, or a set of sub-averages of the sweeps. Avariance of the threshold series, or portion of the series, may be usedin the computation of the confidence limits of the estimated threshold.

Epochs can be rejected if they fail to meet homogeneity criteria, suchas an inter-sweep noise criteria. As discussed previously, homogeneitycriteria can be adjusted based upon the characteristics of all theepochs which have already been collected. Because the size of the signalchanges as the characteristics of the ramping stimuli change,homogeneity criteria can also be created for each column of epochs inthe data matrix, and can be adjusted independently for each column(“intra-column homogeneity criteria”). Intra-column homogeneity criteriaare based upon SNR estimates, or the energy estimated in the signal bin(i.e., a limited amount of data, corresponding to the time of anindividual epoch, from a row of the spectrogram data), while homogeneitycriteria based upon background EEG-noise levels can span across columnssince this measure should not change with the stimulus. Homogeneityestimates here include FFT bin-noise, and SNR estimates as discussedabove. Data matrix definitions include rows and columns. Weightedaveraging, the zero replacement technique, swapping replacementtechnique, repeating replacement technique can all be relied upon inthis procedure. Intra-sweep criteria may be used to reject data from anentire sweep, for example, if more than 3 epochs are replaced due tofailing to meet inter-epoch criteria, and the sweep still contains amuch different amount of noise than the other sweeps, it can be removedfrom the data. If a sweep is removed from the data then any thresholdestimate that was based upon the sweep can be retro-actively removedfrom or modified in the threshold series.

Dynamic Iterative R-AEP Tests

In addition to performing the R-AEP tests in an iterative manner, theactual stimuli can be changed dynamically within a testing procedure.FIG. 7 shows examples of three methods of performing a ramping test. Incolumn A of the figure, the standard ramping stimulus is used in aniterative manner as described above. In column B, the stimuli for aFractionated Ramp Test are shown where the full range of the stimulushas been divided into 3 smaller ranges of intensity. The first stimulusramps between about 40 and about 60 dB SPL, with the second and thirdramping stimuli being decremented in 20 dB steps. For each rampingstimulus, threshold can be estimated, and these results can be combinedand analyzed as a single threshold series or can be analyzed as severalindependent threshold series. In column C, an Adaptive Fractionated RampTest is shown, wherein a ramp stimulus with a large range is first usedto obtain an estimate of threshold, and then a second ramp stimulus isused where the ramp ranges above and below this estimated threshold by,for example, +10 dB and −5 dB respectively, and the slope of the ramphas been decreased. The base signal is the same for the two ranges ofintensity ramps.

The fractionated intensity ramp technique and the adaptive fractionedintensity ramp technique are useful because they allow more data to becollected in an intensity range of interest, which can allow for a moreaccurate estimate of threshold. When the intensity of the stimulus isvery far above threshold, a stable estimate of response amplitude can beobtained quickly, and so not much data may be needed at that range toprovide an initial rough estimate of threshold which can be exploredusing ramping intensity ranges that are appropriate for that subject.Using a dynamic or adaptive technique is advantageous because theresponses to intensities that are very far above threshold, may be lessaccurate in determining threshold, than the medium intensity levelscloser to actual threshold. Further, responses obtained in response tolower intensity stimuli may not be above the noise floor and may bebelow the subject's threshold. Accordingly, by causing the intensityramp to straddle the area of estimated threshold the data that isrecorded can be more relevant and can offer a more reliable estimate ofthreshold.

In FIG. 8 an Adaptive Fractionated Ramp Test is used wherein the lowerintensity ramping stimulus is adjusted based upon the signal and noisecharacteristics of the first test. In the example shown on the left, theaveraged sweep collected in response to the first intensity rampproduces some evoked responses that are “close to the threshold” of thesubject, e.g. within approximately 110-250%. In addition to a thresholdseries, estimates of the amplitude of the response at 60, 55 and 50 dBSPL are obtained by windowing the averaged sweep appropriately. Thedecrease in response amplitude from 60 to 55 dB SPL is not much, but thedecrease from 55 to 50 brings the amplitude of the response close to thenoise floor. This suggests that the lower intensity ramp stimuli areapproaching threshold. Accordingly, the range of the subsequent rampstimulus is decremented only slightly, e.g., by about 5 dB, or not atall.

In the second example, on the right of FIG. 8, the first intensity rampproduces responses that are “far from threshold”, e.g. withinapproximately 210-250%. The responses to the lower intensities of thestimulus are considerably above the noise floor. Accordingly, the rangeof the subsequent ramp stimulus is decremented by about 10 dB. While theexamples show only two repetitions of the adaptive fractionated rampprocedure, more repetitions are possible.

In order to reach significance using an F-ratio (df=2,240), theamplitudes of the evoked responses must be a certain amount, e.g., about170%, of the size of the background EEG noise level estimate. Dependingupon the type of stimulus used, a change in intensity can produce acertain decrease in the amplitude of the responses e.g., decrease ofabout 20% for every 10 dB decrease in intensity. The reduction inintensity can be based upon an expected decrease in the amplitude of theevoked response which would occur with a given change in intensity. Forexample, below 70 dB SPL, the decrease in amplitude which occurs withintensity is about 2 nV/dB, while for noise the reduction is likely 2-4times this. Normative values for the average decrease in amplitude whichis associated with a specified decrease of intensity, based upon theparameters of the ramping stimulus, can be stored in the database. Inone embodiment, the mean amplitude, of the R-AEPS evoked by the highest⅓ of intensity range of the ramping stimulus, is used to adjust themaximum intensity of the subsequent ramping stimulus, so that the R-AEPSevoked by this intensity region are, for example, 250% above the noisefloor.

Accordingly, in one embodiment of the invention, a method of testingauditory function according to the dynamic iterative ramp test procedurecomprises:

a. acoustically presenting at least one ramp stimulus having anintensity range to at least one ear of a subject;

b. recording response data epochs of ramping evoked potential responsedata;

c. classifying the response data epochs into accepted epochs andrejected epochs where the response data epochs are classified asrejected if the epoch fails to meet a homogeneity criteria;

d. performing time-frequency signal analysis on the acceptable responsedata epochs to generate result data;

e. performing steps “a-d” iteratively until criteria such as a noiselevel criteria or a time criteria has been met.

f. using the result data to determine a preliminary audiometricthreshold estimate and using this estimate to determine a usefulintensity range for a second ramp stimulus or using the signal to noiselevels of the result data to determine a useful intensity range for asecond ramp stimulus;

g. performing steps a-d iteratively, and on each iteration, the estimateof the subject's threshold is included in a threshold series for theintensity range, with the iterations being completed when the thresholdseries meets specified threshold criteria.

In step f, using the result data to determine a preliminary audiometricthreshold estimate can utilize the threshold estimated from a thresholdseries.

As has been described previously, in all of the ramping stimulus methodsdescribed, the R-AEP data for each of the different ramp stimuli areorganized, stored, and analyzed in separate data matrices, although theinformation from each of these data matrices can be combined andanalyzed as a whole in order for the software to provide the finalresults of the testing procedure. Further, it should be understood thatthe epochs or sweeps may be identified for noise reduction according toone of the homogeneity criteria described here, such as the inter-epochnoise criteria within or across sweeps, etc. The identified epochs canthen be processed according to one of the noise reduction schemesdescribed here such as the zero replacement technique, the swappingreplacement technique, etc.

In the dynamic iterative ramp tests that have been described, the datacollected to the different ramp stimuli are stored and analyzed indifferent data sets. Accordingly, when analyzing the data evoked inresponse to the second stimulus, the raw data recorded from the firstramp stimulus is usually ignored. This can be less efficient thandesired and can be countered in the following variation to the DynamicIterative Ramp Test procedure. FIG. 9 shows an example of the DynamicIterative Linked Ramp Test technique. The left two graphs of FIG. 9,show the instantaneous intensity of the ramp stimulus and hypotheticalamplitude of the evoked response data, both as a function of time, foran iterative ramping test with a single ramping stimulus. Additionally,the dotted lines on the intensity ramp of the stimulus (labeled “sound1”) show the range of the stimulus that evoked responses at that samepoint in time for the average sweep data. When using a signal processingtechnique to convert the time series data (the averaged sweep) into aspectrogram, such as short time frequency analysis which works byiteratively moving a window through the time series data, the length ofthe window determines the characteristics of the resulting spectrogram.As the data window used to make the spectrogram increases, the range ofthe sound which evoked the response amplitudes that are plotted for acorresponding moment in time on the data graph increases. In the graphof the response data (Data 1), the noise estimate has also been plotted.The amplitudes in epochs 4 and 5 produce responses that are below andabove the noise floor, respectively. On the downward ramp the signalfalls below the noise floor between epochs 12 and 13. Epochs 4 and 5 canbe referred to as a first threshold straddle epoch pair and epochs 12and 13 can be referred to as a second threshold straddle epoch pair. Therange of the intensity ramp for the stimulus for the first and secondthreshold straddle epoch pairs is used to make a symmetrical rampstimulus shown in the graph labeled Sound 2. The epochs from columns 4,5, 12 and 13 are extracted from the data set obtained using sound I(i.e., the Data 1 set) and are used to create sweeps of 4 rather than 16epochs. The new data evoked by the ramp stimulus of Sound 2 is thenstored in sweeps of 4 epochs that correspond the same intensity range ofSound 1. Accordingly, using this technique the data evoked by the firststimulus can be used during the entire testing procedure.

In one embodiment, the method of testing auditory function using aDynamic Iterative Linked Ramp Test procedure comprises:

a. acoustically presenting at least one ramp stimulus having anintensity range to at least one ear of a subject;

b. recording evoked response data organized into epochs;

c. classifying the epochs into accepted epochs and rejected epochs wheresaid an epoch is classified as rejected if the epoch fails in meeting ahomogeneity criteria;

d. performing time-frequency signal analysis on the accepted epochs inorder to generate result data,

e. performing steps “a-d” a specified number of times

f. using the signal to noise levels of the result data to find the firstand second threshold straddle epoch pairs and determine thecorresponding intensity range for use in a second ramp stimulus;

g. rejecting all data epochs that were just recorded which do notcorrespond to the intensity range of the second ramp stimulus, andreorganizing all remaining data epochs so that they correspond with thedata that will be collected with the second ramp stimulus;

h. performing steps “a-d” iteratively using a second ramp stimulus, andon each iteration, the subject's estimated threshold is included in thethreshold series for the intensity range; the iterations being completedwhen the threshold series meets specified threshold criteria.

The examples used thus far for ramping intensity tests have beenillustrated using a single ramping stimulus, for at least one ear. As inthe case of the MASTER technique, it is possible to test several rampingstimuli at once and simultaneously obtain frequency specific data formultiple frequencies. For example, four amplitude modulated carrierfrequencies can be created and then turned into intensity rampingstimuli by multiplying each of these modulated stimuli by a rampingfunction. After the ramping stimuli have been created they can be addedtogether, for example, digitally or acoustically, and then presented toan ear of the subject.

It is also known that different carrier frequencies evoke SS-AEPs ofdifferent sizes. For example, at a stimulus modulation rate of 80 Hz,the responses to amplitude modulated carrier frequencies of 500 Hz aresmaller than the responses obtained to amplitude modulated carrierfrequencies of 1000 Hz. Accordingly, the ranges and ramping functions(e.g., slopes) may be different according to the carrier frequency ofeach ramping stimulus that is combined into a multiple ramping stimulusthat is presented to an ear of a subject. Pilot data, using the MASTERtechnique, has shown that SS-AEPs can be successfully obtained tomultiple stimuli presented simultaneously, when each carrier presentedat a different intensity, as long as the different intensities did notdiffer by more than 20 dB. Accordingly, in one embodiment, the range ofthe intensity ramps for the different ramping stimuli should not differby more than 20 dB.

FIG. 10 shows the same procedure that was demonstrated in FIG. 9, butnow applied to the case where multiple stimuli are presented,simultaneously, to an ear. This technique can also be applied tobinaural testing, but is simplified for this example. In FIG. 10, theIterative Dynamic Linked Ramping Test first uses ramping stimuli whicheach have the same intensity range for the 4 carrier frequencies beingtested. Based upon the analysis of the averaged response data obtainedfrom these ramped stimuli, a second set of ramped stimuli is created.For example, the 500 Hz evoked response data suggested that a thresholdexisted near 42 dB SPL. The epochs from columns 6, 7, 14 and 15, in theresponse data was combined into sweeps of 4 epochs and the responses tothe second ramping stimulus were averaged with this data. For the 1000Hz evoked responses, epochs from columns 2, 3, 10, and 11 wereconcatenated into the sweeps of the data set used to evaluate theresponses to the subsequent ramp stimulus. This is performed in alikewise fashion for the ramping stimuli with carrier frequencies at2000 and 4000 Hz.

Similar to an embodiment of the MASTER technique where the intensitiesof the multiple stimuli are dynamically changed as the associatedSS-AEPs reach significance, the audiology test program 40 organizes thedata matrix so that epoch recorded to different stimuli and/or differentintensities of the stimuli can be analyzed independently.

Modulation Optimization Test (MOT)

In order to perform an SS-AEP threshold or screening test, or a RAMPERtest more efficiently it can be beneficial to utilize modulationfrequencies which produce the largest signal to noise level for a givensubject. For example, Sturzebecher et al, (2003) has recently shownthat, at least for click stimuli, the 140 Hz range, rather than the80-100 Hz range, provided the most robust responses in a group ofinfants. These types of “peaks and valleys” in the size of the responsesover a range of modulation rates have been noted by others. Using a“good” modulation rate will result in response that has much better SNRas a “poor” modulation rate. Accordingly, in order to perform an SS-AEPor RAMPER test, it may first be beneficial to generate a modulationtransfer functions using tonal, noise or click stimuli and then useoptimum modulation frequencies when performing the actual test.

Further, the optimum modulation frequencies for specific carrierfrequencies cab be computed (i.e. predicted) based upon the optimummodulation frequencies obtained to click stimuli. For example, if theoptimum modulation frequency is 110 Hz using click or noise stimuli,then the lower carrier frequencies of the test stimulus, e.g., 500 Hzand 1000 Hz should be modulated at 100 and 105 Hz, while the higherfrequencies, e.g., 2000 and 4000 Hz stimuli should be modulated at 120and 125 Hz. The results of the MOT test can be interpreted utilizingage-appropriate population norms. For example, the prediction of theoptimum modulation rates for certain modulated carriers, which is madeusing the optimum modulation frequencies obtained using click stimuli,can be predicted as a function of age.

The MOT procedure can be used to optimize the subsequent ASSR, MASTER,or RAMPER testing protocol by using modulation rates that produce goodSNRs. Additionally, these testing protocols become more sensitivebecause they avoid the low SNR frequency ranges, or the “null sections”,of an individual, which can cause incorrect identification of hearingimpairment, when these tests are performed without an MOT test. The MOTtest can be performed in a rapid manner by using a greater stimulusintensity than would be used during the subsequent ASSR, MASTER orRAMPER tests. For instance, a MOT test can be completed in approximately3-5 minutes.

To facilitate the MOT test, the ramping methodology described here isapplied to the modulation frequency. The MOT procedure can be donebinaurally, in which the stimuli to the left and right ears may be thesame, or the ramping functions for the stimuli presented to the two earsmay be different. For example, the modulation frequencies for the leftear may ramp from 20 to 100 Hz while the modulation frequencies. for theright ear may ramp from 100 to 200 Hz. In this manner, a greater rangeof modulation frequencies may be tested. Alternatively, if the RAMP isthe same for both ears, e.g. 20 to 100 Hz, the size of the evokedresponse data for the binaural stimulus is larger. Alternatively, theramp may be limited to a small range of modulation rates, such as 75 to100 Hz. In the case where the modulation frequency is ramped from 20 to100 Hz, if the sweep lasts 8.192 seconds then the modulation rate maychange by 10 dB per epoch, whereas if the sweep lasts 16.384 seconds,then the modulation rate will change by 4 dB per epoch. As with anycharacteristic of a ramping stimulus, the rate of the stimulus parameterwhich is being ramped will depend upon the shape of a ramping functionrange of the parameter which is being modified and the sweep length.(e.g., the range of the modulation frequencies to be tested, and theduration of the sweep to be analyzed, will determine the rate of thechange of modulation within the ramping stimulus.) The Data QualityControl Techniques which were described for the intensity ramping testscan be applied to the MOT test as well.

In one embodiment, a method of utilizing a modulation optimization testin order to increase the efficiency, specificity, and sensitivity of anASSR, MASTER, or RAM PER test comprises:

a. performing a modulation optimization test to produce result data inwhich the instantaneous modulation frequency of the stimuli are rampedaccording to a ramping function.

b. analyzing the result data to derive at least one modulation rate withgood SNR characteristics: and;

c. using the at least one modulation rate with good SNR characteristicsfrom step b in an auditory-test such as an ASSR, MASTER, or RAMPER test.

In alternative embodiment of the MOT test, rather than using a rampingstimulus with a continuous ramp, the ramping function is created as astaircase where a different modulation frequency is tested during eachepoch. Alternatively, and SS-AEP test is done where the modulation rateis sequentially changed several times during a test. In thesealternative embodiments, the steps can be summarized as

a. obtain amplitude or SNR estimates for several modulation frequenciesusing stimuli which will create large responses and/or which arepresented at loud intensity levels.

b. choose at least one modulation frequency from the modulation ratesshowing the larger amplitudes or SNRs in a subsequent audiometric testwhich utilizes SS-AEP or R-AEP stimuli.

R-AEP Fine-Structure Test and RAMPER Masking Test

Instead of ramping stimulus intensity over time to determine the hearingthresholds of a subject in order to generate an audiogram, othercapacities of the subject to detect other characteristics of thestimulus can be tested using the testing methods already described,which use time-frequency analysis techniques and ramping stimuli. Forexample, the fine structure of the audiogram may be evaluated using aR-AEP Fine-Structure Test. In the Fine-Structure Test, a singlemodulation rate is used while the frequency of the carrier signal isramped, for example, increasing the carrier frequency over time, from alow frequency (e.g. 200 Hz) to a high frequency (e.g. 4000 Hz). Thecarrier's instantaneous frequency can be ramped in a linear,logarithmic, chirp or other function, or may consist of more than onefunction. The carrier can be signals other than tones, such asband-limited noise whose center frequency is ramped over time. Since themodulation rate remains fixed, only the change in carrier frequency overtime should affect the relative amplitudes of the evoked responses atdifferent moments in time. In an alternative embodiment as the rampingstimulus changes frequency it simultaneously follows an intensity rampso that the intensity of the stimuli remain constant with respect to aspecified intensity type (SPL, nHL, HL). In an alternative embodimentfor different signals, each modulated at a different rate, are rampedsimultaneously. For example, stimuli 1-4 can simultaneously ramp from250-750, 750-1500, 1500-3000, and 3000-6000 Hz, respectively. While therelative R-AEPS to the four ramping stimuli should not be compared,since R-AEP size is a function of modulation rate, the R-AEP results foreach frequency can provide an estimate of the fine structure of theaudiogram. The Fine-Structure Test can be done at several times at oneor more intensity ranges. The results of the Fine-Structure Test, suchas the time-frequency results, the amplitude and phase plots, and thesummary results can be compared to normative values for measures such asthe average slope of the R-AEP amplitude between the 1000 and 2000 Hz.Homogeneity criteria related to the background EEG-noise levels can beused to increase the fidelity of the test.

In a variant of this test, termed the RAM PER Masking Test, two types ofstimuli are presented to an ear. The first stimulus is termed the “test”stimulus and is a steady-state stimulus, for example a 1000 Hz tonemodulated at 80 Hz, the second stimulus is a ramping stimulus, whosecarrier frequency changes over time between two frequencies, forexample, from 500 Hz to 2000 Hz with an instantaneous frequency thatfollows a ramping function. The ramping stimulus may be modulated orun-modulated and may be at the same intensity as the steady-statestimulus, or may be slightly higher or lower in intensity. In this test,the ramping stimulus serves as a masker for the steady-state stimulus,and based upon the changes in amplitude that occur over time in thespectrogram-based analysis, a function can be generated which shouldapproximate the functional (i.e. physiological) tuning curve for thefrequency region of the test stimulus.

A basis of this technique was reported by the Inventor in John et al(1998), where interactions between stimuli were investigated bymeasuring the amplitude of a steady-state response which was attenuatedor amplified by the presence of another steady-state stimulus. Utilizingthe physiological measurement of the interactions which occur betweenstimuli, as a diagnostic test has not been suggested, and is novel.Further, in one embodiment segments of the physiological tuning curvegenerated from this test can be analyzed in several ways, for example,the slope of certain segments of the curve can be measured and comparedto appropriate population normative data. This comparison can lead to anormal/abnormal result. The use of a RAM PER technique and associatedtime-frequency spectrogram-based analysis, used with homogeneitycriteria and EEG-noise level criteria, should reduce the time needed forthis type of test and provide for a more reliable audiometricassessment.

Additionally, in an embodiment of the technique, rather than presentingthe ramping masker stimulus in conjunction with a single probe stimulus,multiple probe stimuli can be presented as occurs in the MASTERtechnique. Accordingly, while the masker stimulus moves away from thecritical band of a probe stimulus at 500 Hz it will simultaneously bemoving towards the critical band of a probe stimulus at 1000 Hz. Thedata quality control techniques which were described for the intensityramping tests can be applied to these tests as well.

Multiple Intensity Test for Estimating Threshold

A method is used to achieve a rapid estimate of a patient's hearingthreshold by evaluating hearing at several intensities simultaneously.In an embodiment of this test, a stimulus is created from severalindividual stimuli each of which is characterized by a differentintensity and modulation rate, and which are combined and simultaneouslypresented to the patient's. This type of stimulus is referred to as aMultiple Intensity Stimulus. For example, in the case of amplitudemodulated stimuli, several modulation functions are used each havingtheir own intensity envelopes and modulation frequencies. These can beeach be applied to a noise carrier (e.g., BBN, HPN, etc.) to produce anamplitude modulated noise stimulus.

An example of how to make a multiple intensity stimulus is as follows: 2or more modulation envelopes of different amplitudes (i.e., intensities)are combined and multiplied with a noise carrier signal to produce astimulus that is modulated at two different rates, each rate having aunique intensity. The modulation depth can be 100%. The response dataevoked by this stimulus will contain responses evoked by stimuli at thedifferent intensities. One difficulty with this technique is that the 2or more modulation envelopes may serve to activate the cochlea at thesame time and thereby reduce the response to each of the envelopecomponents. This can be seen in the top panel of FIG. 11, which shows amultiple intensity stimulus which was created using three sinusoidal AMenvelopes and a noise carrier. The modulation rates for the threedifferent intensities tested were separated by about 5-6 Hz. Asindicated in FIG. 1, the modulation rates were 80.08, 84.96, and 91.8.

In an alternative embodiment of the Multiple Intensity technique, thesimultaneous activation of the cochlea is diminished by usingexponential envelopes with closely spaced modulation frequencies. Thechoice of phase values, for the modulation functions, can also beimportant and correctly chosen phase values. An example of stimuliproduced with these characteristics are shown in the second panel ofFIG. 11. Unlike the stimulus in panel 1, in this example, where 3envelope functions were generated by exponential envelopes with theexponential set at 10, produces less overlap: the three modulationenvelopes, each with its own amplitude (intensity) can be visuallydetected. Alternatively, using 2 envelope functions which are 180degrees out of phase, and which have closely spaced modulationfrequencies produces a multiple intensity envelope with bettercharacteristics (stimuli not shown), because the different intensitiesstimulate the cochlea at different times for a greater portion of thestimulus.

In another embodiment, the testing method addresses the issue ofsimultaneous activation of the cochlea, by using rapid transient stimulirather than steady-state stimuli. Multiple transient stimuli can besimultaneously presented at different repetition rates, where each ratehas a different intensity. Each of the repetition rates should haveinter-stimulus intervals (i.e., the time between the start of sequentialstimuli) that are integer sub-multiples of the epoch length as wasdescribed previously in this material. The 3^(rd) panel of FIG. 11,shows a multiple intensity stimulus using clicks at 2 intensities.

Each column in the figure shows the stimulus over a 50 msec period, withleft spanning 0 to 50 msec and the right spanning 50 to 100 msec. In theupper panel of FIG. 11, the 3 stimuli show considerable overlap, whilein the middle panel the individual 3 envelope functions of differentintensity are more discrete. The bottom panel shows the least amount ofoverlap due to the rapid presentation time of the stimuli.

The responses to a multiple intensity stimulus can be used as ascreening test, by examining whether the response to a stimulus at aparticular intensity is significant. For example, a screening test canconsist of 3 intensities being tested at once, where the middleintensity or lowest intensity is defined as the “screening” intensity Ifthe subject does not show a response to the screening intensity, butdoes show a response to a higher intensity stimulus, which, for example,may be 7 dB above the middle intensity stimulus, then this may indicatethat while the subject failed the screening test, hearing is almostnormal. This may avoid the necessity of a subsequent test beingnecessary. This advantage of this test is similar to that which isobtained for a ramping stimulus technique, in that it may act as acompromise between a screening and threshold test, whereby if thesubject doesn't show evoked responses to an intensity level that isdefined as normal for screening, information regarding the subject'sthreshold is available to inform the medical personnel about how bad thehearing loss may be. Unlike the use of a ramping stimulus, the multipleintensity stimulus is presented simultaneously and at a limited numberof intensities. The separation between the lower to middle intensity andmiddle to high intensity can be identical, or can be different.

The Multiple Intensity Stimulus Test can also be used to estimatethreshold either by considering the significance of the evoked responsesdirectly or by fitting the response data with appropriate regressionequations. Because some masking of the simultaneously presented stimulimay occur due to the temporal overlap and temporal proximity of thestimuli, a weighting factor, or set of weighting factors for eachintensity can be multiplied with each of the evoked responses from thistest prior to their evaluation by means of, for example, regression. Forexample, the amplitude of the responses can be multiplied by a weightingfactor, which can be based upon normative data, for example, 110%, inorder to compensate for decreases in amplitude due to masking. The issueof masking can also be addressed by presenting the multiple intensitystimuli at higher intensity ranges than are normally used in screeningtests, if it can be shown that the information obtained at these higherintensities is relevant to an estimation of actual behavioralthresholds.

The system and methods described here can be used to provide a rapid,reliable, and automatic tests of hearing. These tests include initialscreening evaluations and threshold testing which can be based uponeither frequency-specific or non-frequency-specific criteria. Thesetests include assessing many capacities of an individual's auditorysystem.

Novel types of SS-AEP tests and R-AEP tests utilize stimuli such asmodulated noise and transient stimuli, such as clicks that are presentedat carefully selected repetition rates. The stimuli described evokelarge steady-state responses and thereby increase the speed of theautomatic testing procedure. Methods are described wherein thecharacteristics of the test stimuli can be changed, during a test, forexample, based upon data collected in an early part of the test.

Novel types of methods for processing, accepting, and rejecting theevoked response data are described. Data Quality Control Techniquesincrease the reliability and stability for the estimates both of theevoked responses and of the background EEG-noise levels. Homogeneitycriteria are described.

Novel statistical methods are described which increase the accuracy ofthe tests. Some tests utilize significance series in order to decreasethe occurrence of false positives and false negatives. Some testsutilize a threshold series in order to assess the stability of eachthreshold estimate and to determine how long a test must continue.

Additionally, the use of ramping tests which rely on rapidly presentedramping stimuli to evoke ramping auditory evoked potentials is alsodescribed. Ramping tests can provide a rapid and objective estimate ofthreshold for either frequency specific or-non-frequency specificstimuli. By performing homogeneity testing on the data, rather thansimple artifact rejection criteria, the phase and amplitude plots, whichprovide a measure of the signal at different moments in time, can beused to obtain a better estimate of threshold. Additionally, a novelequation is used to make the phase data of the spectrogram useful in thedetection of the response.

Further, multiple intensity tests are described which can be used toobtain a quick screening test, as well as providing some informationabout a subject's threshold. The tests described often are performedwith multiple stimuli and can be used to test both ears simultaneously.

Rather than utilizing the R-AEP tests and methods described here, whichuse a continuously changing acoustic stimulus and time-frequencyanalysis, the R-AEP tests can be approximated or “mimicked” by obtaininga series of sequential recordings each of which use discrete stimuli andanalyzes result data using only frequency analysis. This less desirablemethod, may, in some cases approximate the information that can beobtained using the ramping stimuli, signal processing analysistechniques, and time-frequency analysis of the R-AEP tests, but willrequire more time, be less accurate. However, the Data Quality ControlTechniques described herein, can be applied both within and across thisseries of sequential tests to improve the data quality, and thereforeimprove Test Results.

The presently described embodiments of the hearing evaluation systemsand methods offer advantages over prior art. Although modifications andchanges may be suggested by those skilled in the art, it is theintention of the inventor to embody within the patent warranted hereinall changes and modifications as reasonably and properly come within thescope of their contribution to the art. The titles, headings, andsubheadings provided in this specification are provided fororganizational purposes only and are not meant to restrict the inventionin any way, nor to limit material described in one section from applyingto another section as would be apparent to those skilled in the art.

Several of references have been described in this patent specification.A full citation is presented below and the contents of the citedreferences are hereby incorporated by reference herein.

REFERENCES

Patent Application

PCT/CA 01/00715 John and Picton, System and Methods For ObjectiveEvaluation Of Hearing Using Auditory Steady-State Responses.

Published Abstracts

John, M S., Brown, D., P. Muir, P., & Picton, T. W., Use of ModulatedNoise in Newborn Hearing Screening. International Evoked ResponseAuditory Study Group (IERASG), 2003b.

Perez-Abalo, M. C., Savio, G., Gonzlez, m, Hernndez, O., Ponce de Leon,M., and Eimil, E., Hearing Screening with Multiple FrequencySteady-State Responses: A Pilot Study. International Evoked ResponseAuditory Study Group (IERASG), 2001.

Scientific Publications

John M. S, Dimitrijevic, A., and Picton, T. W. Efficient Stimuli forEvoking Auditory Steady-State Responses, Ear and Hearing, 24(5):406-23,2003a. John M. S., Dimitrijevic, A., and Picton, T. W. Weightedaveraging of steady-state responses. Clinical Neurophysiology,112:555-562, 2001.

John, M. S., and Picton, T. W. MASTER: A Windows program for recordingmultiple auditory steady-state responses. Computer Methods and Programsin Biomedicine, 61, 125-150, 2000.

John, M. S., Lins, O. G., Boucher, B. L., and Picton, T. W. Multipleauditory steady state responses (MASTER): Stimulus and recordingparameters. Audiology, 37:59-82, 1998.

Linden R D, Campbell K B, Hamel G, Picton T W. Human auditory steadystate evoked potentials during sleep. Ear Hear. 1985 May-June;6(3):167-74.

Norcia A M, Tyler C W. Spatial frequency sweep VEP: visual acuity duringthe first year of life. Vision Res. 1985; 25(10):1399-408.

REFERENCES

Scientific Publications (Continued)

Picton T W, Dimitrijevic A, John M S, Van Roon P. The use of phase inthe detection of auditory steady-state responses. Clin Neurophysiol.2001 September; 112(9):1698-711.

Rees A, Green G G, Kay R H. Steady-state evoked responses tosinusoidally amplitude-modulated sounds recorded in man. Hear Res. 1986;23(2):123-33.

Stapells D R, Oates P. Estimation of the pure-tone audiogram by theauditory brainstem response: a review. Audiol Neurootol. 1997September-October; 2(5):257-80. Review.

Books:

Zar J H. Biostatistical Analysis. Fourth edition. Upper Saddle River:prentice Hall, 1999.

1. A method of performing a fine structure auditory test to evaluate theauditory system of a patient comprising: a. operating a processor toacoustically present at least one acoustic ramp stimulus to at least oneear of the patient, said ramp stimulus having at least two stimuluscharacteristics which ramp over time, and wherein said stimuluscharacteristics are intensity and at least one of the following: carrierfrequency and carrier frequency of a masking stimulus; b. operating aprocessor to record evoked response data from the patient to formresponse data; c. operating a processor to perform signal analysis onsaid response data to generate processed data; d. operating a processorto analyze the result data using time-frequency analysis to produceresult data; and, e. using the result data to evaluate the auditorysystem of said patient.
 2. The method of performing a fine structureauditory test of claim 1 wherein the intensity is ramped as a functionof the frequency ramp in order to provide approximately a constant SPLintensity level.
 3. The method of performing a fine structure auditorytest of claim 1 wherein the intensity is ramped as a function of thefrequency ramp in order to provide approximately a constant HL intensitylevel.
 4. The method of performing a fine structure auditory test ofclaim 2 wherein the test designed to be carried out using an ear inserttransducer.
 5. The method of performing a fine structure auditory testof claim 1 wherein at least one acoustic ramp stimulus comprises atleast 2 stimuli, each having a unique modulation rate.
 6. The method ofperforming a fine structure auditory test of claim 1 wherein at leastone acoustic ramp stimulus comprises at least 3 stimuli, each having aunique modulation rate.
 7. The method of performing a fine structureauditory test of claim 1 wherein at least one acoustic ramp stimuluscomprises at least 4 stimuli, each having a unique modulation rate.